Import Packages and Files

In [1]:
import torch.nn as nn
from functionalities import dataloader as dl
from functionalities import evaluater as ev
from functionalities import filemanager as fm
from functionalities import trainer as tr
from functionalities import plot as p
from architecture import RotNet as RN

Load Dataset

In [ ]:
trainset, testset, classes = dl.load_cifar("./datasets")
In [ ]:
trainloader, validloader, testloader = dl.make_dataloaders(trainset, testset, 128)

Initialize Loss Criterion

In [ ]:
criterion = nn.CrossEntropyLoss()

Train RotNet for Rotation Task and Classifiers on Feature Maps

In [ ]:
# set rot classes
rot_classes = ['original', '90 rotation', '180 rotation', '270 rotation']

3 Block RotNet

In [ ]:
# initialize network
net_block3 = RN.RotNet(num_classes=4, num_conv_block=3, add_avg_pool=False)
In [13]:
# train network
rot_block3_loss_log, _, rot_block3_test_accuracy_log, _, _ = tr.adaptive_learning([0.1, 0.02, 0.004, 0.0008], 
    [60, 120, 160, 200], 0.9, 5e-4, net_block3, criterion, trainloader, None, testloader, rot=['90', '180', '270'])
[1, 60] loss: 1.141
[1, 120] loss: 0.999
[1, 180] loss: 0.920
[1, 240] loss: 0.851
[1, 300] loss: 0.790
[1, 360] loss: 0.759
Epoch: 1 -> Loss: 0.787678480148
Epoch: 1 -> Test Accuracy: 69.21
[2, 60] loss: 0.701
[2, 120] loss: 0.696
[2, 180] loss: 0.677
[2, 240] loss: 0.648
[2, 300] loss: 0.647
[2, 360] loss: 0.616
Epoch: 2 -> Loss: 0.662011623383
Epoch: 2 -> Test Accuracy: 76.055
[3, 60] loss: 0.596
[3, 120] loss: 0.581
[3, 180] loss: 0.577
[3, 240] loss: 0.583
[3, 300] loss: 0.557
[3, 360] loss: 0.566
Epoch: 3 -> Loss: 0.581205248833
Epoch: 3 -> Test Accuracy: 78.8075
[4, 60] loss: 0.544
[4, 120] loss: 0.519
[4, 180] loss: 0.517
[4, 240] loss: 0.537
[4, 300] loss: 0.507
[4, 360] loss: 0.507
Epoch: 4 -> Loss: 0.558216452599
Epoch: 4 -> Test Accuracy: 79.01
[5, 60] loss: 0.496
[5, 120] loss: 0.497
[5, 180] loss: 0.486
[5, 240] loss: 0.495
[5, 300] loss: 0.473
[5, 360] loss: 0.472
Epoch: 5 -> Loss: 0.537355840206
Epoch: 5 -> Test Accuracy: 81.725
[6, 60] loss: 0.449
[6, 120] loss: 0.464
[6, 180] loss: 0.462
[6, 240] loss: 0.465
[6, 300] loss: 0.466
[6, 360] loss: 0.452
Epoch: 6 -> Loss: 0.470865309238
Epoch: 6 -> Test Accuracy: 81.4725
[7, 60] loss: 0.439
[7, 120] loss: 0.433
[7, 180] loss: 0.429
[7, 240] loss: 0.451
[7, 300] loss: 0.439
[7, 360] loss: 0.450
Epoch: 7 -> Loss: 0.556775391102
Epoch: 7 -> Test Accuracy: 83.0
[8, 60] loss: 0.432
[8, 120] loss: 0.434
[8, 180] loss: 0.416
[8, 240] loss: 0.422
[8, 300] loss: 0.417
[8, 360] loss: 0.429
Epoch: 8 -> Loss: 0.499394506216
Epoch: 8 -> Test Accuracy: 82.2925
[9, 60] loss: 0.406
[9, 120] loss: 0.397
[9, 180] loss: 0.410
[9, 240] loss: 0.417
[9, 300] loss: 0.413
[9, 360] loss: 0.406
Epoch: 9 -> Loss: 0.406752169132
Epoch: 9 -> Test Accuracy: 82.8125
[10, 60] loss: 0.386
[10, 120] loss: 0.398
[10, 180] loss: 0.404
[10, 240] loss: 0.402
[10, 300] loss: 0.401
[10, 360] loss: 0.402
Epoch: 10 -> Loss: 0.341294229031
Epoch: 10 -> Test Accuracy: 84.595
[11, 60] loss: 0.385
[11, 120] loss: 0.395
[11, 180] loss: 0.400
[11, 240] loss: 0.379
[11, 300] loss: 0.391
[11, 360] loss: 0.401
Epoch: 11 -> Loss: 0.273233801126
Epoch: 11 -> Test Accuracy: 84.7425
[12, 60] loss: 0.388
[12, 120] loss: 0.380
[12, 180] loss: 0.389
[12, 240] loss: 0.374
[12, 300] loss: 0.375
[12, 360] loss: 0.389
Epoch: 12 -> Loss: 0.283909648657
Epoch: 12 -> Test Accuracy: 84.285
[13, 60] loss: 0.380
[13, 120] loss: 0.366
[13, 180] loss: 0.369
[13, 240] loss: 0.375
[13, 300] loss: 0.378
[13, 360] loss: 0.368
Epoch: 13 -> Loss: 0.423889309168
Epoch: 13 -> Test Accuracy: 84.8575
[14, 60] loss: 0.345
[14, 120] loss: 0.374
[14, 180] loss: 0.375
[14, 240] loss: 0.373
[14, 300] loss: 0.385
[14, 360] loss: 0.377
Epoch: 14 -> Loss: 0.427053511143
Epoch: 14 -> Test Accuracy: 85.68
[15, 60] loss: 0.357
[15, 120] loss: 0.350
[15, 180] loss: 0.352
[15, 240] loss: 0.354
[15, 300] loss: 0.373
[15, 360] loss: 0.377
Epoch: 15 -> Loss: 0.273929357529
Epoch: 15 -> Test Accuracy: 85.0625
[16, 60] loss: 0.342
[16, 120] loss: 0.355
[16, 180] loss: 0.359
[16, 240] loss: 0.362
[16, 300] loss: 0.356
[16, 360] loss: 0.351
Epoch: 16 -> Loss: 0.370511502028
Epoch: 16 -> Test Accuracy: 85.84
[17, 60] loss: 0.340
[17, 120] loss: 0.351
[17, 180] loss: 0.350
[17, 240] loss: 0.339
[17, 300] loss: 0.372
[17, 360] loss: 0.364
Epoch: 17 -> Loss: 0.440175831318
Epoch: 17 -> Test Accuracy: 85.1375
[18, 60] loss: 0.346
[18, 120] loss: 0.324
[18, 180] loss: 0.364
[18, 240] loss: 0.361
[18, 300] loss: 0.339
[18, 360] loss: 0.349
Epoch: 18 -> Loss: 0.436338275671
Epoch: 18 -> Test Accuracy: 84.84
[19, 60] loss: 0.345
[19, 120] loss: 0.332
[19, 180] loss: 0.351
[19, 240] loss: 0.348
[19, 300] loss: 0.347
[19, 360] loss: 0.353
Epoch: 19 -> Loss: 0.369504094124
Epoch: 19 -> Test Accuracy: 85.5425
[20, 60] loss: 0.332
[20, 120] loss: 0.338
[20, 180] loss: 0.358
[20, 240] loss: 0.338
[20, 300] loss: 0.341
[20, 360] loss: 0.350
Epoch: 20 -> Loss: 0.225799173117
Epoch: 20 -> Test Accuracy: 86.325
[21, 60] loss: 0.337
[21, 120] loss: 0.341
[21, 180] loss: 0.337
[21, 240] loss: 0.341
[21, 300] loss: 0.334
[21, 360] loss: 0.337
Epoch: 21 -> Loss: 0.392786115408
Epoch: 21 -> Test Accuracy: 86.0875
[22, 60] loss: 0.336
[22, 120] loss: 0.334
[22, 180] loss: 0.334
[22, 240] loss: 0.339
[22, 300] loss: 0.337
[22, 360] loss: 0.330
Epoch: 22 -> Loss: 0.356912195683
Epoch: 22 -> Test Accuracy: 85.8025
[23, 60] loss: 0.309
[23, 120] loss: 0.337
[23, 180] loss: 0.343
[23, 240] loss: 0.327
[23, 300] loss: 0.342
[23, 360] loss: 0.333
Epoch: 23 -> Loss: 0.468298614025
Epoch: 23 -> Test Accuracy: 85.7675
[24, 60] loss: 0.332
[24, 120] loss: 0.321
[24, 180] loss: 0.338
[24, 240] loss: 0.338
[24, 300] loss: 0.337
[24, 360] loss: 0.332
Epoch: 24 -> Loss: 0.208731681108
Epoch: 24 -> Test Accuracy: 86.345
[25, 60] loss: 0.330
[25, 120] loss: 0.324
[25, 180] loss: 0.336
[25, 240] loss: 0.326
[25, 300] loss: 0.336
[25, 360] loss: 0.329
Epoch: 25 -> Loss: 0.385006994009
Epoch: 25 -> Test Accuracy: 85.9125
[26, 60] loss: 0.318
[26, 120] loss: 0.318
[26, 180] loss: 0.328
[26, 240] loss: 0.324
[26, 300] loss: 0.331
[26, 360] loss: 0.334
Epoch: 26 -> Loss: 0.294116735458
Epoch: 26 -> Test Accuracy: 86.9775
[27, 60] loss: 0.305
[27, 120] loss: 0.330
[27, 180] loss: 0.327
[27, 240] loss: 0.331
[27, 300] loss: 0.339
[27, 360] loss: 0.319
Epoch: 27 -> Loss: 0.295988678932
Epoch: 27 -> Test Accuracy: 86.8975
[28, 60] loss: 0.330
[28, 120] loss: 0.316
[28, 180] loss: 0.325
[28, 240] loss: 0.324
[28, 300] loss: 0.318
[28, 360] loss: 0.342
Epoch: 28 -> Loss: 0.233337074518
Epoch: 28 -> Test Accuracy: 86.135
[29, 60] loss: 0.317
[29, 120] loss: 0.330
[29, 180] loss: 0.321
[29, 240] loss: 0.322
[29, 300] loss: 0.332
[29, 360] loss: 0.313
Epoch: 29 -> Loss: 0.352734535933
Epoch: 29 -> Test Accuracy: 86.1825
[30, 60] loss: 0.299
[30, 120] loss: 0.326
[30, 180] loss: 0.321
[30, 240] loss: 0.328
[30, 300] loss: 0.327
[30, 360] loss: 0.329
Epoch: 30 -> Loss: 0.335489243269
Epoch: 30 -> Test Accuracy: 86.6575
[31, 60] loss: 0.306
[31, 120] loss: 0.327
[31, 180] loss: 0.323
[31, 240] loss: 0.322
[31, 300] loss: 0.326
[31, 360] loss: 0.315
Epoch: 31 -> Loss: 0.512306988239
Epoch: 31 -> Test Accuracy: 85.37
[32, 60] loss: 0.297
[32, 120] loss: 0.316
[32, 180] loss: 0.340
[32, 240] loss: 0.321
[32, 300] loss: 0.310
[32, 360] loss: 0.321
Epoch: 32 -> Loss: 0.35365241766
Epoch: 32 -> Test Accuracy: 86.9825
[33, 60] loss: 0.319
[33, 120] loss: 0.318
[33, 180] loss: 0.328
[33, 240] loss: 0.319
[33, 300] loss: 0.313
[33, 360] loss: 0.321
Epoch: 33 -> Loss: 0.385478198528
Epoch: 33 -> Test Accuracy: 86.2025
[34, 60] loss: 0.318
[34, 120] loss: 0.316
[34, 180] loss: 0.300
[34, 240] loss: 0.324
[34, 300] loss: 0.319
[34, 360] loss: 0.311
Epoch: 34 -> Loss: 0.343362241983
Epoch: 34 -> Test Accuracy: 85.1975
[35, 60] loss: 0.309
[35, 120] loss: 0.325
[35, 180] loss: 0.327
[35, 240] loss: 0.322
[35, 300] loss: 0.320
[35, 360] loss: 0.320
Epoch: 35 -> Loss: 0.272876352072
Epoch: 35 -> Test Accuracy: 86.99
[36, 60] loss: 0.307
[36, 120] loss: 0.308
[36, 180] loss: 0.311
[36, 240] loss: 0.322
[36, 300] loss: 0.314
[36, 360] loss: 0.322
Epoch: 36 -> Loss: 0.267319113016
Epoch: 36 -> Test Accuracy: 86.2175
[37, 60] loss: 0.304
[37, 120] loss: 0.319
[37, 180] loss: 0.311
[37, 240] loss: 0.317
[37, 300] loss: 0.317
[37, 360] loss: 0.314
Epoch: 37 -> Loss: 0.215385004878
Epoch: 37 -> Test Accuracy: 87.205
[38, 60] loss: 0.300
[38, 120] loss: 0.300
[38, 180] loss: 0.300
[38, 240] loss: 0.317
[38, 300] loss: 0.318
[38, 360] loss: 0.315
Epoch: 38 -> Loss: 0.333074420691
Epoch: 38 -> Test Accuracy: 86.69
[39, 60] loss: 0.298
[39, 120] loss: 0.313
[39, 180] loss: 0.312
[39, 240] loss: 0.314
[39, 300] loss: 0.312
[39, 360] loss: 0.322
Epoch: 39 -> Loss: 0.329833418131
Epoch: 39 -> Test Accuracy: 87.2125
[40, 60] loss: 0.298
[40, 120] loss: 0.299
[40, 180] loss: 0.309
[40, 240] loss: 0.317
[40, 300] loss: 0.317
[40, 360] loss: 0.316
Epoch: 40 -> Loss: 0.324939012527
Epoch: 40 -> Test Accuracy: 87.0475
[41, 60] loss: 0.303
[41, 120] loss: 0.304
[41, 180] loss: 0.305
[41, 240] loss: 0.304
[41, 300] loss: 0.327
[41, 360] loss: 0.309
Epoch: 41 -> Loss: 0.334853470325
Epoch: 41 -> Test Accuracy: 86.5025
[42, 60] loss: 0.309
[42, 120] loss: 0.306
[42, 180] loss: 0.314
[42, 240] loss: 0.322
[42, 300] loss: 0.311
[42, 360] loss: 0.313
Epoch: 42 -> Loss: 0.318148314953
Epoch: 42 -> Test Accuracy: 87.27
[43, 60] loss: 0.284
[43, 120] loss: 0.312
[43, 180] loss: 0.322
[43, 240] loss: 0.291
[43, 300] loss: 0.321
[43, 360] loss: 0.304
Epoch: 43 -> Loss: 0.382598012686
Epoch: 43 -> Test Accuracy: 83.9325
[44, 60] loss: 0.311
[44, 120] loss: 0.305
[44, 180] loss: 0.303
[44, 240] loss: 0.298
[44, 300] loss: 0.310
[44, 360] loss: 0.330
Epoch: 44 -> Loss: 0.220706671476
Epoch: 44 -> Test Accuracy: 87.075
[45, 60] loss: 0.298
[45, 120] loss: 0.296
[45, 180] loss: 0.313
[45, 240] loss: 0.317
[45, 300] loss: 0.302
[45, 360] loss: 0.308
Epoch: 45 -> Loss: 0.32049909234
Epoch: 45 -> Test Accuracy: 87.4575
[46, 60] loss: 0.290
[46, 120] loss: 0.306
[46, 180] loss: 0.302
[46, 240] loss: 0.307
[46, 300] loss: 0.309
[46, 360] loss: 0.316
Epoch: 46 -> Loss: 0.323618233204
Epoch: 46 -> Test Accuracy: 87.1525
[47, 60] loss: 0.294
[47, 120] loss: 0.285
[47, 180] loss: 0.300
[47, 240] loss: 0.322
[47, 300] loss: 0.311
[47, 360] loss: 0.307
Epoch: 47 -> Loss: 0.383451044559
Epoch: 47 -> Test Accuracy: 86.2075
[48, 60] loss: 0.297
[48, 120] loss: 0.300
[48, 180] loss: 0.293
[48, 240] loss: 0.324
[48, 300] loss: 0.312
[48, 360] loss: 0.307
Epoch: 48 -> Loss: 0.247455790639
Epoch: 48 -> Test Accuracy: 87.21
[49, 60] loss: 0.280
[49, 120] loss: 0.305
[49, 180] loss: 0.303
[49, 240] loss: 0.310
[49, 300] loss: 0.311
[49, 360] loss: 0.316
Epoch: 49 -> Loss: 0.177563875914
Epoch: 49 -> Test Accuracy: 86.7775
[50, 60] loss: 0.310
[50, 120] loss: 0.299
[50, 180] loss: 0.295
[50, 240] loss: 0.303
[50, 300] loss: 0.308
[50, 360] loss: 0.317
Epoch: 50 -> Loss: 0.344310581684
Epoch: 50 -> Test Accuracy: 87.055
[51, 60] loss: 0.287
[51, 120] loss: 0.293
[51, 180] loss: 0.309
[51, 240] loss: 0.303
[51, 300] loss: 0.308
[51, 360] loss: 0.311
Epoch: 51 -> Loss: 0.226258903742
Epoch: 51 -> Test Accuracy: 87.18
[52, 60] loss: 0.288
[52, 120] loss: 0.309
[52, 180] loss: 0.302
[52, 240] loss: 0.311
[52, 300] loss: 0.306
[52, 360] loss: 0.306
Epoch: 52 -> Loss: 0.207420393825
Epoch: 52 -> Test Accuracy: 85.79
[53, 60] loss: 0.296
[53, 120] loss: 0.301
[53, 180] loss: 0.297
[53, 240] loss: 0.297
[53, 300] loss: 0.304
[53, 360] loss: 0.310
Epoch: 53 -> Loss: 0.364464432001
Epoch: 53 -> Test Accuracy: 87.105
[54, 60] loss: 0.300
[54, 120] loss: 0.304
[54, 180] loss: 0.313
[54, 240] loss: 0.303
[54, 300] loss: 0.302
[54, 360] loss: 0.298
Epoch: 54 -> Loss: 0.423810869455
Epoch: 54 -> Test Accuracy: 86.735
[55, 60] loss: 0.292
[55, 120] loss: 0.302
[55, 180] loss: 0.307
[55, 240] loss: 0.300
[55, 300] loss: 0.294
[55, 360] loss: 0.310
Epoch: 55 -> Loss: 0.418384850025
Epoch: 55 -> Test Accuracy: 87.13
[56, 60] loss: 0.289
[56, 120] loss: 0.310
[56, 180] loss: 0.288
[56, 240] loss: 0.301
[56, 300] loss: 0.298
[56, 360] loss: 0.308
Epoch: 56 -> Loss: 0.383456289768
Epoch: 56 -> Test Accuracy: 86.5475
[57, 60] loss: 0.295
[57, 120] loss: 0.302
[57, 180] loss: 0.305
[57, 240] loss: 0.306
[57, 300] loss: 0.297
[57, 360] loss: 0.298
Epoch: 57 -> Loss: 0.171087294817
Epoch: 57 -> Test Accuracy: 87.1125
[58, 60] loss: 0.280
[58, 120] loss: 0.290
[58, 180] loss: 0.306
[58, 240] loss: 0.300
[58, 300] loss: 0.307
[58, 360] loss: 0.291
Epoch: 58 -> Loss: 0.33591324091
Epoch: 58 -> Test Accuracy: 86.4525
[59, 60] loss: 0.285
[59, 120] loss: 0.292
[59, 180] loss: 0.299
[59, 240] loss: 0.301
[59, 300] loss: 0.307
[59, 360] loss: 0.308
Epoch: 59 -> Loss: 0.359701931477
Epoch: 59 -> Test Accuracy: 86.6825
[60, 60] loss: 0.296
[60, 120] loss: 0.296
[60, 180] loss: 0.294
[60, 240] loss: 0.295
[60, 300] loss: 0.304
[60, 360] loss: 0.283
Epoch: 60 -> Loss: 0.189138680696
Epoch: 60 -> Test Accuracy: 86.9475
[61, 60] loss: 0.233
[61, 120] loss: 0.200
[61, 180] loss: 0.192
[61, 240] loss: 0.179
[61, 300] loss: 0.185
[61, 360] loss: 0.176
Epoch: 61 -> Loss: 0.169828921556
Epoch: 61 -> Test Accuracy: 91.1325
[62, 60] loss: 0.152
[62, 120] loss: 0.171
[62, 180] loss: 0.173
[62, 240] loss: 0.170
[62, 300] loss: 0.175
[62, 360] loss: 0.172
Epoch: 62 -> Loss: 0.253974795341
Epoch: 62 -> Test Accuracy: 91.3325
[63, 60] loss: 0.144
[63, 120] loss: 0.151
[63, 180] loss: 0.154
[63, 240] loss: 0.161
[63, 300] loss: 0.159
[63, 360] loss: 0.156
Epoch: 63 -> Loss: 0.121799066663
Epoch: 63 -> Test Accuracy: 91.0675
[64, 60] loss: 0.147
[64, 120] loss: 0.150
[64, 180] loss: 0.157
[64, 240] loss: 0.149
[64, 300] loss: 0.159
[64, 360] loss: 0.152
Epoch: 64 -> Loss: 0.203890949488
Epoch: 64 -> Test Accuracy: 91.1125
[65, 60] loss: 0.148
[65, 120] loss: 0.141
[65, 180] loss: 0.142
[65, 240] loss: 0.159
[65, 300] loss: 0.150
[65, 360] loss: 0.143
Epoch: 65 -> Loss: 0.0973534584045
Epoch: 65 -> Test Accuracy: 91.3175
[66, 60] loss: 0.138
[66, 120] loss: 0.137
[66, 180] loss: 0.146
[66, 240] loss: 0.144
[66, 300] loss: 0.156
[66, 360] loss: 0.154
Epoch: 66 -> Loss: 0.157145500183
Epoch: 66 -> Test Accuracy: 90.9675
[67, 60] loss: 0.140
[67, 120] loss: 0.142
[67, 180] loss: 0.138
[67, 240] loss: 0.143
[67, 300] loss: 0.155
[67, 360] loss: 0.150
Epoch: 67 -> Loss: 0.144269049168
Epoch: 67 -> Test Accuracy: 91.22
[68, 60] loss: 0.138
[68, 120] loss: 0.141
[68, 180] loss: 0.138
[68, 240] loss: 0.142
[68, 300] loss: 0.149
[68, 360] loss: 0.156
Epoch: 68 -> Loss: 0.115117274225
Epoch: 68 -> Test Accuracy: 90.9275
[69, 60] loss: 0.130
[69, 120] loss: 0.150
[69, 180] loss: 0.140
[69, 240] loss: 0.147
[69, 300] loss: 0.149
[69, 360] loss: 0.151
Epoch: 69 -> Loss: 0.166590347886
Epoch: 69 -> Test Accuracy: 91.17
[70, 60] loss: 0.134
[70, 120] loss: 0.135
[70, 180] loss: 0.146
[70, 240] loss: 0.142
[70, 300] loss: 0.153
[70, 360] loss: 0.151
Epoch: 70 -> Loss: 0.12411685288
Epoch: 70 -> Test Accuracy: 91.2375
[71, 60] loss: 0.135
[71, 120] loss: 0.138
[71, 180] loss: 0.142
[71, 240] loss: 0.148
[71, 300] loss: 0.149
[71, 360] loss: 0.150
Epoch: 71 -> Loss: 0.111194655299
Epoch: 71 -> Test Accuracy: 90.845
[72, 60] loss: 0.138
[72, 120] loss: 0.142
[72, 180] loss: 0.139
[72, 240] loss: 0.145
[72, 300] loss: 0.151
[72, 360] loss: 0.158
Epoch: 72 -> Loss: 0.115612111986
Epoch: 72 -> Test Accuracy: 90.725
[73, 60] loss: 0.136
[73, 120] loss: 0.148
[73, 180] loss: 0.146
[73, 240] loss: 0.137
[73, 300] loss: 0.145
[73, 360] loss: 0.153
Epoch: 73 -> Loss: 0.173922792077
Epoch: 73 -> Test Accuracy: 90.495
[74, 60] loss: 0.137
[74, 120] loss: 0.132
[74, 180] loss: 0.148
[74, 240] loss: 0.156
[74, 300] loss: 0.160
[74, 360] loss: 0.153
Epoch: 74 -> Loss: 0.161368072033
Epoch: 74 -> Test Accuracy: 90.6275
[75, 60] loss: 0.137
[75, 120] loss: 0.132
[75, 180] loss: 0.149
[75, 240] loss: 0.152
[75, 300] loss: 0.149
[75, 360] loss: 0.144
Epoch: 75 -> Loss: 0.264967113733
Epoch: 75 -> Test Accuracy: 90.45
[76, 60] loss: 0.135
[76, 120] loss: 0.142
[76, 180] loss: 0.146
[76, 240] loss: 0.158
[76, 300] loss: 0.145
[76, 360] loss: 0.151
Epoch: 76 -> Loss: 0.18966922164
Epoch: 76 -> Test Accuracy: 90.3475
[77, 60] loss: 0.134
[77, 120] loss: 0.135
[77, 180] loss: 0.137
[77, 240] loss: 0.144
[77, 300] loss: 0.159
[77, 360] loss: 0.164
Epoch: 77 -> Loss: 0.165032312274
Epoch: 77 -> Test Accuracy: 89.9725
[78, 60] loss: 0.137
[78, 120] loss: 0.137
[78, 180] loss: 0.150
[78, 240] loss: 0.148
[78, 300] loss: 0.148
[78, 360] loss: 0.147
Epoch: 78 -> Loss: 0.118702635169
Epoch: 78 -> Test Accuracy: 90.21
[79, 60] loss: 0.142
[79, 120] loss: 0.139
[79, 180] loss: 0.141
[79, 240] loss: 0.145
[79, 300] loss: 0.145
[79, 360] loss: 0.157
Epoch: 79 -> Loss: 0.200592786074
Epoch: 79 -> Test Accuracy: 90.805
[80, 60] loss: 0.146
[80, 120] loss: 0.140
[80, 180] loss: 0.146
[80, 240] loss: 0.144
[80, 300] loss: 0.146
[80, 360] loss: 0.163
Epoch: 80 -> Loss: 0.0748644471169
Epoch: 80 -> Test Accuracy: 90.3125
[81, 60] loss: 0.147
[81, 120] loss: 0.146
[81, 180] loss: 0.151
[81, 240] loss: 0.139
[81, 300] loss: 0.150
[81, 360] loss: 0.157
Epoch: 81 -> Loss: 0.112988092005
Epoch: 81 -> Test Accuracy: 90.3075
[82, 60] loss: 0.138
[82, 120] loss: 0.138
[82, 180] loss: 0.152
[82, 240] loss: 0.144
[82, 300] loss: 0.144
[82, 360] loss: 0.154
Epoch: 82 -> Loss: 0.0915526524186
Epoch: 82 -> Test Accuracy: 90.405
[83, 60] loss: 0.130
[83, 120] loss: 0.140
[83, 180] loss: 0.146
[83, 240] loss: 0.145
[83, 300] loss: 0.153
[83, 360] loss: 0.158
Epoch: 83 -> Loss: 0.156742066145
Epoch: 83 -> Test Accuracy: 90.6825
[84, 60] loss: 0.135
[84, 120] loss: 0.139
[84, 180] loss: 0.137
[84, 240] loss: 0.145
[84, 300] loss: 0.158
[84, 360] loss: 0.149
Epoch: 84 -> Loss: 0.114132240415
Epoch: 84 -> Test Accuracy: 90.4775
[85, 60] loss: 0.129
[85, 120] loss: 0.129
[85, 180] loss: 0.137
[85, 240] loss: 0.145
[85, 300] loss: 0.148
[85, 360] loss: 0.152
Epoch: 85 -> Loss: 0.170121192932
Epoch: 85 -> Test Accuracy: 90.3325
[86, 60] loss: 0.132
[86, 120] loss: 0.145
[86, 180] loss: 0.141
[86, 240] loss: 0.146
[86, 300] loss: 0.144
[86, 360] loss: 0.155
Epoch: 86 -> Loss: 0.139373719692
Epoch: 86 -> Test Accuracy: 90.41
[87, 60] loss: 0.136
[87, 120] loss: 0.136
[87, 180] loss: 0.147
[87, 240] loss: 0.148
[87, 300] loss: 0.145
[87, 360] loss: 0.157
Epoch: 87 -> Loss: 0.234496861696
Epoch: 87 -> Test Accuracy: 89.7625
[88, 60] loss: 0.134
[88, 120] loss: 0.139
[88, 180] loss: 0.140
[88, 240] loss: 0.139
[88, 300] loss: 0.155
[88, 360] loss: 0.162
Epoch: 88 -> Loss: 0.169020205736
Epoch: 88 -> Test Accuracy: 90.3625
[89, 60] loss: 0.132
[89, 120] loss: 0.142
[89, 180] loss: 0.144
[89, 240] loss: 0.148
[89, 300] loss: 0.141
[89, 360] loss: 0.141
Epoch: 89 -> Loss: 0.257692873478
Epoch: 89 -> Test Accuracy: 90.35
[90, 60] loss: 0.135
[90, 120] loss: 0.136
[90, 180] loss: 0.148
[90, 240] loss: 0.139
[90, 300] loss: 0.152
[90, 360] loss: 0.150
Epoch: 90 -> Loss: 0.183051347733
Epoch: 90 -> Test Accuracy: 90.4525
[91, 60] loss: 0.127
[91, 120] loss: 0.136
[91, 180] loss: 0.146
[91, 240] loss: 0.146
[91, 300] loss: 0.143
[91, 360] loss: 0.146
Epoch: 91 -> Loss: 0.230325505137
Epoch: 91 -> Test Accuracy: 90.575
[92, 60] loss: 0.137
[92, 120] loss: 0.130
[92, 180] loss: 0.133
[92, 240] loss: 0.142
[92, 300] loss: 0.144
[92, 360] loss: 0.150
Epoch: 92 -> Loss: 0.177278190851
Epoch: 92 -> Test Accuracy: 90.1125
[93, 60] loss: 0.124
[93, 120] loss: 0.132
[93, 180] loss: 0.143
[93, 240] loss: 0.145
[93, 300] loss: 0.138
[93, 360] loss: 0.150
Epoch: 93 -> Loss: 0.115009739995
Epoch: 93 -> Test Accuracy: 90.295
[94, 60] loss: 0.132
[94, 120] loss: 0.133
[94, 180] loss: 0.134
[94, 240] loss: 0.152
[94, 300] loss: 0.152
[94, 360] loss: 0.144
Epoch: 94 -> Loss: 0.123808979988
Epoch: 94 -> Test Accuracy: 90.675
[95, 60] loss: 0.140
[95, 120] loss: 0.130
[95, 180] loss: 0.147
[95, 240] loss: 0.143
[95, 300] loss: 0.146
[95, 360] loss: 0.145
Epoch: 95 -> Loss: 0.0961346998811
Epoch: 95 -> Test Accuracy: 90.2625
[96, 60] loss: 0.130
[96, 120] loss: 0.129
[96, 180] loss: 0.139
[96, 240] loss: 0.147
[96, 300] loss: 0.152
[96, 360] loss: 0.144
Epoch: 96 -> Loss: 0.0893204286695
Epoch: 96 -> Test Accuracy: 90.52
[97, 60] loss: 0.124
[97, 120] loss: 0.135
[97, 180] loss: 0.141
[97, 240] loss: 0.137
[97, 300] loss: 0.133
[97, 360] loss: 0.146
Epoch: 97 -> Loss: 0.141617566347
Epoch: 97 -> Test Accuracy: 91.06
[98, 60] loss: 0.125
[98, 120] loss: 0.140
[98, 180] loss: 0.143
[98, 240] loss: 0.136
[98, 300] loss: 0.143
[98, 360] loss: 0.150
Epoch: 98 -> Loss: 0.166911140084
Epoch: 98 -> Test Accuracy: 90.305
[99, 60] loss: 0.129
[99, 120] loss: 0.130
[99, 180] loss: 0.136
[99, 240] loss: 0.139
[99, 300] loss: 0.149
[99, 360] loss: 0.140
Epoch: 99 -> Loss: 0.151396125555
Epoch: 99 -> Test Accuracy: 89.95
[100, 60] loss: 0.130
[100, 120] loss: 0.133
[100, 180] loss: 0.135
[100, 240] loss: 0.144
[100, 300] loss: 0.147
[100, 360] loss: 0.154
Epoch: 100 -> Loss: 0.127764731646
Epoch: 100 -> Test Accuracy: 90.2675
[101, 60] loss: 0.135
[101, 120] loss: 0.125
[101, 180] loss: 0.139
[101, 240] loss: 0.136
[101, 300] loss: 0.151
[101, 360] loss: 0.141
Epoch: 101 -> Loss: 0.155829519033
Epoch: 101 -> Test Accuracy: 90.58
[102, 60] loss: 0.127
[102, 120] loss: 0.132
[102, 180] loss: 0.140
[102, 240] loss: 0.142
[102, 300] loss: 0.142
[102, 360] loss: 0.148
Epoch: 102 -> Loss: 0.149779215455
Epoch: 102 -> Test Accuracy: 90.5275
[103, 60] loss: 0.123
[103, 120] loss: 0.132
[103, 180] loss: 0.137
[103, 240] loss: 0.136
[103, 300] loss: 0.139
[103, 360] loss: 0.144
Epoch: 103 -> Loss: 0.143297225237
Epoch: 103 -> Test Accuracy: 89.9525
[104, 60] loss: 0.137
[104, 120] loss: 0.121
[104, 180] loss: 0.140
[104, 240] loss: 0.140
[104, 300] loss: 0.133
[104, 360] loss: 0.147
Epoch: 104 -> Loss: 0.165147423744
Epoch: 104 -> Test Accuracy: 90.55
[105, 60] loss: 0.127
[105, 120] loss: 0.120
[105, 180] loss: 0.136
[105, 240] loss: 0.147
[105, 300] loss: 0.143
[105, 360] loss: 0.145
Epoch: 105 -> Loss: 0.153953403234
Epoch: 105 -> Test Accuracy: 90.44
[106, 60] loss: 0.120
[106, 120] loss: 0.128
[106, 180] loss: 0.131
[106, 240] loss: 0.136
[106, 300] loss: 0.137
[106, 360] loss: 0.139
Epoch: 106 -> Loss: 0.160528451204
Epoch: 106 -> Test Accuracy: 91.0075
[107, 60] loss: 0.127
[107, 120] loss: 0.121
[107, 180] loss: 0.132
[107, 240] loss: 0.139
[107, 300] loss: 0.142
[107, 360] loss: 0.142
Epoch: 107 -> Loss: 0.0822361558676
Epoch: 107 -> Test Accuracy: 90.775
[108, 60] loss: 0.134
[108, 120] loss: 0.134
[108, 180] loss: 0.135
[108, 240] loss: 0.143
[108, 300] loss: 0.129
[108, 360] loss: 0.143
Epoch: 108 -> Loss: 0.14577370882
Epoch: 108 -> Test Accuracy: 90.03
[109, 60] loss: 0.122
[109, 120] loss: 0.129
[109, 180] loss: 0.130
[109, 240] loss: 0.133
[109, 300] loss: 0.133
[109, 360] loss: 0.149
Epoch: 109 -> Loss: 0.171248614788
Epoch: 109 -> Test Accuracy: 90.3875
[110, 60] loss: 0.128
[110, 120] loss: 0.130
[110, 180] loss: 0.142
[110, 240] loss: 0.139
[110, 300] loss: 0.136
[110, 360] loss: 0.143
Epoch: 110 -> Loss: 0.190067365766
Epoch: 110 -> Test Accuracy: 90.57
[111, 60] loss: 0.121
[111, 120] loss: 0.133
[111, 180] loss: 0.132
[111, 240] loss: 0.138
[111, 300] loss: 0.141
[111, 360] loss: 0.134
Epoch: 111 -> Loss: 0.136060848832
Epoch: 111 -> Test Accuracy: 90.3075
[112, 60] loss: 0.129
[112, 120] loss: 0.127
[112, 180] loss: 0.136
[112, 240] loss: 0.134
[112, 300] loss: 0.141
[112, 360] loss: 0.135
Epoch: 112 -> Loss: 0.13542817533
Epoch: 112 -> Test Accuracy: 90.8225
[113, 60] loss: 0.117
[113, 120] loss: 0.127
[113, 180] loss: 0.133
[113, 240] loss: 0.142
[113, 300] loss: 0.142
[113, 360] loss: 0.131
Epoch: 113 -> Loss: 0.221937775612
Epoch: 113 -> Test Accuracy: 89.9675
[114, 60] loss: 0.129
[114, 120] loss: 0.131
[114, 180] loss: 0.135
[114, 240] loss: 0.133
[114, 300] loss: 0.139
[114, 360] loss: 0.143
Epoch: 114 -> Loss: 0.0751198902726
Epoch: 114 -> Test Accuracy: 90.23
[115, 60] loss: 0.126
[115, 120] loss: 0.116
[115, 180] loss: 0.129
[115, 240] loss: 0.137
[115, 300] loss: 0.134
[115, 360] loss: 0.141
Epoch: 115 -> Loss: 0.137918055058
Epoch: 115 -> Test Accuracy: 90.8375
[116, 60] loss: 0.124
[116, 120] loss: 0.134
[116, 180] loss: 0.136
[116, 240] loss: 0.128
[116, 300] loss: 0.131
[116, 360] loss: 0.139
Epoch: 116 -> Loss: 0.073376826942
Epoch: 116 -> Test Accuracy: 90.7075
[117, 60] loss: 0.121
[117, 120] loss: 0.128
[117, 180] loss: 0.124
[117, 240] loss: 0.139
[117, 300] loss: 0.137
[117, 360] loss: 0.143
Epoch: 117 -> Loss: 0.135540992022
Epoch: 117 -> Test Accuracy: 90.095
[118, 60] loss: 0.130
[118, 120] loss: 0.129
[118, 180] loss: 0.127
[118, 240] loss: 0.135
[118, 300] loss: 0.140
[118, 360] loss: 0.138
Epoch: 118 -> Loss: 0.0981592684984
Epoch: 118 -> Test Accuracy: 90.3075
[119, 60] loss: 0.130
[119, 120] loss: 0.132
[119, 180] loss: 0.130
[119, 240] loss: 0.127
[119, 300] loss: 0.137
[119, 360] loss: 0.133
Epoch: 119 -> Loss: 0.136474281549
Epoch: 119 -> Test Accuracy: 90.315
[120, 60] loss: 0.121
[120, 120] loss: 0.132
[120, 180] loss: 0.125
[120, 240] loss: 0.141
[120, 300] loss: 0.133
[120, 360] loss: 0.128
Epoch: 120 -> Loss: 0.0627753213048
Epoch: 120 -> Test Accuracy: 90.1825
[121, 60] loss: 0.096
[121, 120] loss: 0.081
[121, 180] loss: 0.072
[121, 240] loss: 0.071
[121, 300] loss: 0.069
[121, 360] loss: 0.075
Epoch: 121 -> Loss: 0.0725773051381
Epoch: 121 -> Test Accuracy: 92.2175
[122, 60] loss: 0.064
[122, 120] loss: 0.059
[122, 180] loss: 0.062
[122, 240] loss: 0.062
[122, 300] loss: 0.055
[122, 360] loss: 0.059
Epoch: 122 -> Loss: 0.0669576302171
Epoch: 122 -> Test Accuracy: 92.225
[123, 60] loss: 0.053
[123, 120] loss: 0.055
[123, 180] loss: 0.057
[123, 240] loss: 0.050
[123, 300] loss: 0.053
[123, 360] loss: 0.053
Epoch: 123 -> Loss: 0.0238902159035
Epoch: 123 -> Test Accuracy: 92.405
[124, 60] loss: 0.047
[124, 120] loss: 0.048
[124, 180] loss: 0.047
[124, 240] loss: 0.051
[124, 300] loss: 0.050
[124, 360] loss: 0.052
Epoch: 124 -> Loss: 0.0751750022173
Epoch: 124 -> Test Accuracy: 92.46
[125, 60] loss: 0.043
[125, 120] loss: 0.044
[125, 180] loss: 0.046
[125, 240] loss: 0.046
[125, 300] loss: 0.050
[125, 360] loss: 0.045
Epoch: 125 -> Loss: 0.0497423000634
Epoch: 125 -> Test Accuracy: 92.345
[126, 60] loss: 0.039
[126, 120] loss: 0.044
[126, 180] loss: 0.041
[126, 240] loss: 0.044
[126, 300] loss: 0.045
[126, 360] loss: 0.044
Epoch: 126 -> Loss: 0.0285705067217
Epoch: 126 -> Test Accuracy: 92.0975
[127, 60] loss: 0.040
[127, 120] loss: 0.041
[127, 180] loss: 0.038
[127, 240] loss: 0.040
[127, 300] loss: 0.042
[127, 360] loss: 0.044
Epoch: 127 -> Loss: 0.0293492916971
Epoch: 127 -> Test Accuracy: 92.2325
[128, 60] loss: 0.038
[128, 120] loss: 0.039
[128, 180] loss: 0.036
[128, 240] loss: 0.039
[128, 300] loss: 0.037
[128, 360] loss: 0.041
Epoch: 128 -> Loss: 0.0484538264573
Epoch: 128 -> Test Accuracy: 92.2175
[129, 60] loss: 0.038
[129, 120] loss: 0.038
[129, 180] loss: 0.038
[129, 240] loss: 0.037
[129, 300] loss: 0.038
[129, 360] loss: 0.038
Epoch: 129 -> Loss: 0.0514101460576
Epoch: 129 -> Test Accuracy: 92.2875
[130, 60] loss: 0.036
[130, 120] loss: 0.038
[130, 180] loss: 0.032
[130, 240] loss: 0.037
[130, 300] loss: 0.037
[130, 360] loss: 0.037
Epoch: 130 -> Loss: 0.0424250736833
Epoch: 130 -> Test Accuracy: 92.1325
[131, 60] loss: 0.032
[131, 120] loss: 0.034
[131, 180] loss: 0.034
[131, 240] loss: 0.033
[131, 300] loss: 0.033
[131, 360] loss: 0.033
Epoch: 131 -> Loss: 0.0391034409404
Epoch: 131 -> Test Accuracy: 92.2525
[132, 60] loss: 0.033
[132, 120] loss: 0.033
[132, 180] loss: 0.035
[132, 240] loss: 0.036
[132, 300] loss: 0.032
[132, 360] loss: 0.036
Epoch: 132 -> Loss: 0.0384169593453
Epoch: 132 -> Test Accuracy: 92.165
[133, 60] loss: 0.029
[133, 120] loss: 0.030
[133, 180] loss: 0.031
[133, 240] loss: 0.031
[133, 300] loss: 0.032
[133, 360] loss: 0.037
Epoch: 133 -> Loss: 0.014396908693
Epoch: 133 -> Test Accuracy: 92.16
[134, 60] loss: 0.033
[134, 120] loss: 0.031
[134, 180] loss: 0.032
[134, 240] loss: 0.033
[134, 300] loss: 0.033
[134, 360] loss: 0.033
Epoch: 134 -> Loss: 0.0565491244197
Epoch: 134 -> Test Accuracy: 92.0725
[135, 60] loss: 0.030
[135, 120] loss: 0.029
[135, 180] loss: 0.031
[135, 240] loss: 0.031
[135, 300] loss: 0.030
[135, 360] loss: 0.031
Epoch: 135 -> Loss: 0.0157452970743
Epoch: 135 -> Test Accuracy: 92.22
[136, 60] loss: 0.030
[136, 120] loss: 0.031
[136, 180] loss: 0.029
[136, 240] loss: 0.030
[136, 300] loss: 0.030
[136, 360] loss: 0.029
Epoch: 136 -> Loss: 0.0522452518344
Epoch: 136 -> Test Accuracy: 92.26
[137, 60] loss: 0.028
[137, 120] loss: 0.027
[137, 180] loss: 0.029
[137, 240] loss: 0.031
[137, 300] loss: 0.031
[137, 360] loss: 0.032
Epoch: 137 -> Loss: 0.0168153364211
Epoch: 137 -> Test Accuracy: 92.0925
[138, 60] loss: 0.026
[138, 120] loss: 0.025
[138, 180] loss: 0.028
[138, 240] loss: 0.032
[138, 300] loss: 0.029
[138, 360] loss: 0.029
Epoch: 138 -> Loss: 0.0144899655133
Epoch: 138 -> Test Accuracy: 92.1325
[139, 60] loss: 0.030
[139, 120] loss: 0.030
[139, 180] loss: 0.028
[139, 240] loss: 0.029
[139, 300] loss: 0.029
[139, 360] loss: 0.029
Epoch: 139 -> Loss: 0.0294731017202
Epoch: 139 -> Test Accuracy: 92.0575
[140, 60] loss: 0.028
[140, 120] loss: 0.027
[140, 180] loss: 0.025
[140, 240] loss: 0.030
[140, 300] loss: 0.029
[140, 360] loss: 0.030
Epoch: 140 -> Loss: 0.0386446416378
Epoch: 140 -> Test Accuracy: 92.1675
[141, 60] loss: 0.026
[141, 120] loss: 0.026
[141, 180] loss: 0.027
[141, 240] loss: 0.029
[141, 300] loss: 0.025
[141, 360] loss: 0.026
Epoch: 141 -> Loss: 0.0462640114129
Epoch: 141 -> Test Accuracy: 92.0175
[142, 60] loss: 0.024
[142, 120] loss: 0.026
[142, 180] loss: 0.023
[142, 240] loss: 0.026
[142, 300] loss: 0.028
[142, 360] loss: 0.027
Epoch: 142 -> Loss: 0.0198109000921
Epoch: 142 -> Test Accuracy: 92.19
[143, 60] loss: 0.025
[143, 120] loss: 0.026
[143, 180] loss: 0.029
[143, 240] loss: 0.028
[143, 300] loss: 0.026
[143, 360] loss: 0.027
Epoch: 143 -> Loss: 0.0327403433621
Epoch: 143 -> Test Accuracy: 92.03
[144, 60] loss: 0.024
[144, 120] loss: 0.026
[144, 180] loss: 0.027
[144, 240] loss: 0.025
[144, 300] loss: 0.027
[144, 360] loss: 0.027
Epoch: 144 -> Loss: 0.0148653211072
Epoch: 144 -> Test Accuracy: 91.95
[145, 60] loss: 0.024
[145, 120] loss: 0.023
[145, 180] loss: 0.024
[145, 240] loss: 0.026
[145, 300] loss: 0.024
[145, 360] loss: 0.028
Epoch: 145 -> Loss: 0.0257855504751
Epoch: 145 -> Test Accuracy: 92.075
[146, 60] loss: 0.025
[146, 120] loss: 0.025
[146, 180] loss: 0.024
[146, 240] loss: 0.025
[146, 300] loss: 0.026
[146, 360] loss: 0.024
Epoch: 146 -> Loss: 0.0358236059546
Epoch: 146 -> Test Accuracy: 92.0125
[147, 60] loss: 0.025
[147, 120] loss: 0.023
[147, 180] loss: 0.025
[147, 240] loss: 0.026
[147, 300] loss: 0.026
[147, 360] loss: 0.028
Epoch: 147 -> Loss: 0.0306266844273
Epoch: 147 -> Test Accuracy: 91.9525
[148, 60] loss: 0.023
[148, 120] loss: 0.023
[148, 180] loss: 0.025
[148, 240] loss: 0.023
[148, 300] loss: 0.029
[148, 360] loss: 0.027
Epoch: 148 -> Loss: 0.0236614309251
Epoch: 148 -> Test Accuracy: 92.1275
[149, 60] loss: 0.025
[149, 120] loss: 0.023
[149, 180] loss: 0.024
[149, 240] loss: 0.025
[149, 300] loss: 0.028
[149, 360] loss: 0.027
Epoch: 149 -> Loss: 0.013561449945
Epoch: 149 -> Test Accuracy: 91.935
[150, 60] loss: 0.024
[150, 120] loss: 0.024
[150, 180] loss: 0.025
[150, 240] loss: 0.025
[150, 300] loss: 0.026
[150, 360] loss: 0.025
Epoch: 150 -> Loss: 0.0366351529956
Epoch: 150 -> Test Accuracy: 92.0575
[151, 60] loss: 0.022
[151, 120] loss: 0.024
[151, 180] loss: 0.023
[151, 240] loss: 0.025
[151, 300] loss: 0.025
[151, 360] loss: 0.027
Epoch: 151 -> Loss: 0.0221746247262
Epoch: 151 -> Test Accuracy: 92.03
[152, 60] loss: 0.022
[152, 120] loss: 0.024
[152, 180] loss: 0.025
[152, 240] loss: 0.022
[152, 300] loss: 0.026
[152, 360] loss: 0.024
Epoch: 152 -> Loss: 0.0234043058008
Epoch: 152 -> Test Accuracy: 91.7625
[153, 60] loss: 0.023
[153, 120] loss: 0.022
[153, 180] loss: 0.023
[153, 240] loss: 0.025
[153, 300] loss: 0.023
[153, 360] loss: 0.026
Epoch: 153 -> Loss: 0.0341323800385
Epoch: 153 -> Test Accuracy: 91.845
[154, 60] loss: 0.023
[154, 120] loss: 0.023
[154, 180] loss: 0.024
[154, 240] loss: 0.025
[154, 300] loss: 0.024
[154, 360] loss: 0.024
Epoch: 154 -> Loss: 0.0257519632578
Epoch: 154 -> Test Accuracy: 91.755
[155, 60] loss: 0.022
[155, 120] loss: 0.023
[155, 180] loss: 0.026
[155, 240] loss: 0.024
[155, 300] loss: 0.025
[155, 360] loss: 0.026
Epoch: 155 -> Loss: 0.0171002503484
Epoch: 155 -> Test Accuracy: 91.93
[156, 60] loss: 0.021
[156, 120] loss: 0.021
[156, 180] loss: 0.024
[156, 240] loss: 0.024
[156, 300] loss: 0.023
[156, 360] loss: 0.026
Epoch: 156 -> Loss: 0.0187948737293
Epoch: 156 -> Test Accuracy: 91.6375
[157, 60] loss: 0.021
[157, 120] loss: 0.022
[157, 180] loss: 0.023
[157, 240] loss: 0.022
[157, 300] loss: 0.025
[157, 360] loss: 0.025
Epoch: 157 -> Loss: 0.0213442686945
Epoch: 157 -> Test Accuracy: 91.82
[158, 60] loss: 0.025
[158, 120] loss: 0.025
[158, 180] loss: 0.025
[158, 240] loss: 0.026
[158, 300] loss: 0.025
[158, 360] loss: 0.028
Epoch: 158 -> Loss: 0.0364259406924
Epoch: 158 -> Test Accuracy: 92.09
[159, 60] loss: 0.023
[159, 120] loss: 0.025
[159, 180] loss: 0.024
[159, 240] loss: 0.023
[159, 300] loss: 0.024
[159, 360] loss: 0.026
Epoch: 159 -> Loss: 0.0178028680384
Epoch: 159 -> Test Accuracy: 91.6875
[160, 60] loss: 0.024
[160, 120] loss: 0.023
[160, 180] loss: 0.023
[160, 240] loss: 0.025
[160, 300] loss: 0.027
[160, 360] loss: 0.026
Epoch: 160 -> Loss: 0.0754204690456
Epoch: 160 -> Test Accuracy: 91.7
[161, 60] loss: 0.021
[161, 120] loss: 0.019
[161, 180] loss: 0.019
[161, 240] loss: 0.017
[161, 300] loss: 0.017
[161, 360] loss: 0.016
Epoch: 161 -> Loss: 0.0247515030205
Epoch: 161 -> Test Accuracy: 92.1075
[162, 60] loss: 0.013
[162, 120] loss: 0.015
[162, 180] loss: 0.016
[162, 240] loss: 0.015
[162, 300] loss: 0.015
[162, 360] loss: 0.014
Epoch: 162 -> Loss: 0.0142389312387
Epoch: 162 -> Test Accuracy: 92.19
[163, 60] loss: 0.014
[163, 120] loss: 0.014
[163, 180] loss: 0.014
[163, 240] loss: 0.014
[163, 300] loss: 0.015
[163, 360] loss: 0.013
Epoch: 163 -> Loss: 0.0101729640737
Epoch: 163 -> Test Accuracy: 92.195
[164, 60] loss: 0.013
[164, 120] loss: 0.013
[164, 180] loss: 0.013
[164, 240] loss: 0.013
[164, 300] loss: 0.013
[164, 360] loss: 0.014
Epoch: 164 -> Loss: 0.0181327145547
Epoch: 164 -> Test Accuracy: 92.16
[165, 60] loss: 0.013
[165, 120] loss: 0.013
[165, 180] loss: 0.013
[165, 240] loss: 0.012
[165, 300] loss: 0.012
[165, 360] loss: 0.013
Epoch: 165 -> Loss: 0.00228249793872
Epoch: 165 -> Test Accuracy: 92.2325
[166, 60] loss: 0.013
[166, 120] loss: 0.012
[166, 180] loss: 0.012
[166, 240] loss: 0.013
[166, 300] loss: 0.013
[166, 360] loss: 0.012
Epoch: 166 -> Loss: 0.00735792284831
Epoch: 166 -> Test Accuracy: 92.18
[167, 60] loss: 0.012
[167, 120] loss: 0.011
[167, 180] loss: 0.013
[167, 240] loss: 0.012
[167, 300] loss: 0.013
[167, 360] loss: 0.012
Epoch: 167 -> Loss: 0.00716293137521
Epoch: 167 -> Test Accuracy: 92.15
[168, 60] loss: 0.012
[168, 120] loss: 0.010
[168, 180] loss: 0.012
[168, 240] loss: 0.012
[168, 300] loss: 0.012
[168, 360] loss: 0.013
Epoch: 168 -> Loss: 0.0218465216458
Epoch: 168 -> Test Accuracy: 92.205
[169, 60] loss: 0.012
[169, 120] loss: 0.010
[169, 180] loss: 0.011
[169, 240] loss: 0.012
[169, 300] loss: 0.011
[169, 360] loss: 0.011
Epoch: 169 -> Loss: 0.0141726005822
Epoch: 169 -> Test Accuracy: 92.3075
[170, 60] loss: 0.012
[170, 120] loss: 0.012
[170, 180] loss: 0.012
[170, 240] loss: 0.011
[170, 300] loss: 0.012
[170, 360] loss: 0.011
Epoch: 170 -> Loss: 0.0133976582438
Epoch: 170 -> Test Accuracy: 92.21
[171, 60] loss: 0.011
[171, 120] loss: 0.011
[171, 180] loss: 0.012
[171, 240] loss: 0.011
[171, 300] loss: 0.011
[171, 360] loss: 0.012
Epoch: 171 -> Loss: 0.0172377824783
Epoch: 171 -> Test Accuracy: 92.2825
[172, 60] loss: 0.010
[172, 120] loss: 0.011
[172, 180] loss: 0.011
[172, 240] loss: 0.011
[172, 300] loss: 0.011
[172, 360] loss: 0.012
Epoch: 172 -> Loss: 0.00986809376627
Epoch: 172 -> Test Accuracy: 92.2425
[173, 60] loss: 0.010
[173, 120] loss: 0.010
[173, 180] loss: 0.011
[173, 240] loss: 0.012
[173, 300] loss: 0.010
[173, 360] loss: 0.011
Epoch: 173 -> Loss: 0.0149984331802
Epoch: 173 -> Test Accuracy: 92.2925
[174, 60] loss: 0.011
[174, 120] loss: 0.011
[174, 180] loss: 0.011
[174, 240] loss: 0.011
[174, 300] loss: 0.010
[174, 360] loss: 0.011
Epoch: 174 -> Loss: 0.00998050533235
Epoch: 174 -> Test Accuracy: 92.2625
[175, 60] loss: 0.010
[175, 120] loss: 0.011
[175, 180] loss: 0.012
[175, 240] loss: 0.011
[175, 300] loss: 0.010
[175, 360] loss: 0.010
Epoch: 175 -> Loss: 0.0101813357323
Epoch: 175 -> Test Accuracy: 92.215
[176, 60] loss: 0.010
[176, 120] loss: 0.010
[176, 180] loss: 0.011
[176, 240] loss: 0.011
[176, 300] loss: 0.010
[176, 360] loss: 0.011
Epoch: 176 -> Loss: 0.011940584518
Epoch: 176 -> Test Accuracy: 92.2125
[177, 60] loss: 0.010
[177, 120] loss: 0.011
[177, 180] loss: 0.010
[177, 240] loss: 0.011
[177, 300] loss: 0.010
[177, 360] loss: 0.010
Epoch: 177 -> Loss: 0.00595696875826
Epoch: 177 -> Test Accuracy: 92.25
[178, 60] loss: 0.011
[178, 120] loss: 0.011
[178, 180] loss: 0.010
[178, 240] loss: 0.010
[178, 300] loss: 0.010
[178, 360] loss: 0.010
Epoch: 178 -> Loss: 0.00822608359158
Epoch: 178 -> Test Accuracy: 92.2025
[179, 60] loss: 0.010
[179, 120] loss: 0.010
[179, 180] loss: 0.010
[179, 240] loss: 0.011
[179, 300] loss: 0.010
[179, 360] loss: 0.010
Epoch: 179 -> Loss: 0.00978438556194
Epoch: 179 -> Test Accuracy: 92.21
[180, 60] loss: 0.010
[180, 120] loss: 0.010
[180, 180] loss: 0.010
[180, 240] loss: 0.010
[180, 300] loss: 0.011
[180, 360] loss: 0.010
Epoch: 180 -> Loss: 0.00779106328264
Epoch: 180 -> Test Accuracy: 92.2375
[181, 60] loss: 0.010
[181, 120] loss: 0.010
[181, 180] loss: 0.011
[181, 240] loss: 0.010
[181, 300] loss: 0.010
[181, 360] loss: 0.010
Epoch: 181 -> Loss: 0.00954251550138
Epoch: 181 -> Test Accuracy: 92.25
[182, 60] loss: 0.010
[182, 120] loss: 0.010
[182, 180] loss: 0.011
[182, 240] loss: 0.010
[182, 300] loss: 0.010
[182, 360] loss: 0.011
Epoch: 182 -> Loss: 0.00503360107541
Epoch: 182 -> Test Accuracy: 92.2475
[183, 60] loss: 0.011
[183, 120] loss: 0.010
[183, 180] loss: 0.009
[183, 240] loss: 0.010
[183, 300] loss: 0.011
[183, 360] loss: 0.011
Epoch: 183 -> Loss: 0.0226580798626
Epoch: 183 -> Test Accuracy: 92.21
[184, 60] loss: 0.009
[184, 120] loss: 0.010
[184, 180] loss: 0.010
[184, 240] loss: 0.010
[184, 300] loss: 0.009
[184, 360] loss: 0.010
Epoch: 184 -> Loss: 0.0174920354038
Epoch: 184 -> Test Accuracy: 92.275
[185, 60] loss: 0.010
[185, 120] loss: 0.009
[185, 180] loss: 0.010
[185, 240] loss: 0.010
[185, 300] loss: 0.010
[185, 360] loss: 0.010
Epoch: 185 -> Loss: 0.00887467432767
Epoch: 185 -> Test Accuracy: 92.2
[186, 60] loss: 0.009
[186, 120] loss: 0.009
[186, 180] loss: 0.010
[186, 240] loss: 0.010
[186, 300] loss: 0.010
[186, 360] loss: 0.011
Epoch: 186 -> Loss: 0.0100764958188
Epoch: 186 -> Test Accuracy: 92.2775
[187, 60] loss: 0.009
[187, 120] loss: 0.009
[187, 180] loss: 0.010
[187, 240] loss: 0.010
[187, 300] loss: 0.010
[187, 360] loss: 0.010
Epoch: 187 -> Loss: 0.00767189497128
Epoch: 187 -> Test Accuracy: 92.2925
[188, 60] loss: 0.009
[188, 120] loss: 0.011
[188, 180] loss: 0.009
[188, 240] loss: 0.009
[188, 300] loss: 0.009
[188, 360] loss: 0.010
Epoch: 188 -> Loss: 0.0170344524086
Epoch: 188 -> Test Accuracy: 92.23
[189, 60] loss: 0.009
[189, 120] loss: 0.009
[189, 180] loss: 0.010
[189, 240] loss: 0.010
[189, 300] loss: 0.010
[189, 360] loss: 0.010
Epoch: 189 -> Loss: 0.0175261907279
Epoch: 189 -> Test Accuracy: 92.2
[190, 60] loss: 0.010
[190, 120] loss: 0.010
[190, 180] loss: 0.009
[190, 240] loss: 0.009
[190, 300] loss: 0.009
[190, 360] loss: 0.010
Epoch: 190 -> Loss: 0.0128434095532
Epoch: 190 -> Test Accuracy: 92.21
[191, 60] loss: 0.010
[191, 120] loss: 0.010
[191, 180] loss: 0.009
[191, 240] loss: 0.010
[191, 300] loss: 0.010
[191, 360] loss: 0.009
Epoch: 191 -> Loss: 0.0104228034616
Epoch: 191 -> Test Accuracy: 92.185
[192, 60] loss: 0.010
[192, 120] loss: 0.010
[192, 180] loss: 0.010
[192, 240] loss: 0.010
[192, 300] loss: 0.010
[192, 360] loss: 0.009
Epoch: 192 -> Loss: 0.00403060019016
Epoch: 192 -> Test Accuracy: 92.1775
[193, 60] loss: 0.009
[193, 120] loss: 0.010
[193, 180] loss: 0.010
[193, 240] loss: 0.009
[193, 300] loss: 0.009
[193, 360] loss: 0.009
Epoch: 193 -> Loss: 0.0098797082901
Epoch: 193 -> Test Accuracy: 92.1575
[194, 60] loss: 0.009
[194, 120] loss: 0.009
[194, 180] loss: 0.010
[194, 240] loss: 0.010
[194, 300] loss: 0.009
[194, 360] loss: 0.010
Epoch: 194 -> Loss: 0.00674241501838
Epoch: 194 -> Test Accuracy: 92.1775
[195, 60] loss: 0.010
[195, 120] loss: 0.009
[195, 180] loss: 0.009
[195, 240] loss: 0.010
[195, 300] loss: 0.009
[195, 360] loss: 0.009
Epoch: 195 -> Loss: 0.00723540503532
Epoch: 195 -> Test Accuracy: 92.145
[196, 60] loss: 0.009
[196, 120] loss: 0.009
[196, 180] loss: 0.009
[196, 240] loss: 0.009
[196, 300] loss: 0.010
[196, 360] loss: 0.010
Epoch: 196 -> Loss: 0.00891743786633
Epoch: 196 -> Test Accuracy: 92.1725
[197, 60] loss: 0.009
[197, 120] loss: 0.009
[197, 180] loss: 0.009
[197, 240] loss: 0.010
[197, 300] loss: 0.009
[197, 360] loss: 0.010
Epoch: 197 -> Loss: 0.0150486528873
Epoch: 197 -> Test Accuracy: 92.1625
[198, 60] loss: 0.008
[198, 120] loss: 0.009
[198, 180] loss: 0.009
[198, 240] loss: 0.010
[198, 300] loss: 0.009
[198, 360] loss: 0.009
Epoch: 198 -> Loss: 0.00717498268932
Epoch: 198 -> Test Accuracy: 92.175
[199, 60] loss: 0.009
[199, 120] loss: 0.009
[199, 180] loss: 0.009
[199, 240] loss: 0.010
[199, 300] loss: 0.009
[199, 360] loss: 0.010
Epoch: 199 -> Loss: 0.00609672721475
Epoch: 199 -> Test Accuracy: 92.165
[200, 60] loss: 0.009
[200, 120] loss: 0.009
[200, 180] loss: 0.009
[200, 240] loss: 0.010
[200, 300] loss: 0.009
[200, 360] loss: 0.009
Epoch: 200 -> Loss: 0.00567238777876
Epoch: 200 -> Test Accuracy: 92.19
Finished Training
In [14]:
# train NonLinearClassifiers on feature map of net_3block
block3_loss_log, _, block3_test_accuracy_log, _, _ = tr.train_all_blocks(3, 10, [0.1, 0.02, 0.004, 0.0008], 
    [20, 40, 45, 100], 0.9, 5e-4, net_block3, criterion, trainloader, None, testloader) 
[1, 60] loss: 2.135
[1, 120] loss: 1.237
[1, 180] loss: 1.118
[1, 240] loss: 1.054
[1, 300] loss: 1.027
[1, 360] loss: 0.987
Epoch: 1 -> Loss: 1.02406382561
Epoch: 1 -> Test Accuracy: 68.73
[2, 60] loss: 0.908
[2, 120] loss: 0.900
[2, 180] loss: 0.892
[2, 240] loss: 0.870
[2, 300] loss: 0.870
[2, 360] loss: 0.830
Epoch: 2 -> Loss: 0.819397449493
Epoch: 2 -> Test Accuracy: 72.48
[3, 60] loss: 0.808
[3, 120] loss: 0.809
[3, 180] loss: 0.789
[3, 240] loss: 0.807
[3, 300] loss: 0.777
[3, 360] loss: 0.760
Epoch: 3 -> Loss: 0.705734670162
Epoch: 3 -> Test Accuracy: 74.34
[4, 60] loss: 0.757
[4, 120] loss: 0.763
[4, 180] loss: 0.744
[4, 240] loss: 0.729
[4, 300] loss: 0.732
[4, 360] loss: 0.722
Epoch: 4 -> Loss: 0.572060704231
Epoch: 4 -> Test Accuracy: 75.96
[5, 60] loss: 0.705
[5, 120] loss: 0.700
[5, 180] loss: 0.710
[5, 240] loss: 0.693
[5, 300] loss: 0.706
[5, 360] loss: 0.720
Epoch: 5 -> Loss: 0.530122101307
Epoch: 5 -> Test Accuracy: 76.62
[6, 60] loss: 0.681
[6, 120] loss: 0.698
[6, 180] loss: 0.682
[6, 240] loss: 0.688
[6, 300] loss: 0.689
[6, 360] loss: 0.684
Epoch: 6 -> Loss: 0.762083053589
Epoch: 6 -> Test Accuracy: 77.13
[7, 60] loss: 0.647
[7, 120] loss: 0.658
[7, 180] loss: 0.682
[7, 240] loss: 0.670
[7, 300] loss: 0.683
[7, 360] loss: 0.668
Epoch: 7 -> Loss: 0.561828672886
Epoch: 7 -> Test Accuracy: 76.95
[8, 60] loss: 0.643
[8, 120] loss: 0.646
[8, 180] loss: 0.652
[8, 240] loss: 0.667
[8, 300] loss: 0.651
[8, 360] loss: 0.660
Epoch: 8 -> Loss: 0.631638646126
Epoch: 8 -> Test Accuracy: 77.92
[9, 60] loss: 0.617
[9, 120] loss: 0.627
[9, 180] loss: 0.632
[9, 240] loss: 0.663
[9, 300] loss: 0.652
[9, 360] loss: 0.653
Epoch: 9 -> Loss: 0.601558744907
Epoch: 9 -> Test Accuracy: 77.99
[10, 60] loss: 0.607
[10, 120] loss: 0.628
[10, 180] loss: 0.627
[10, 240] loss: 0.632
[10, 300] loss: 0.629
[10, 360] loss: 0.639
Epoch: 10 -> Loss: 0.69752061367
Epoch: 10 -> Test Accuracy: 77.83
[11, 60] loss: 0.615
[11, 120] loss: 0.618
[11, 180] loss: 0.613
[11, 240] loss: 0.626
[11, 300] loss: 0.623
[11, 360] loss: 0.626
Epoch: 11 -> Loss: 0.627347946167
Epoch: 11 -> Test Accuracy: 78.23
[12, 60] loss: 0.615
[12, 120] loss: 0.603
[12, 180] loss: 0.591
[12, 240] loss: 0.627
[12, 300] loss: 0.607
[12, 360] loss: 0.641
Epoch: 12 -> Loss: 0.440225422382
Epoch: 12 -> Test Accuracy: 78.78
[13, 60] loss: 0.601
[13, 120] loss: 0.604
[13, 180] loss: 0.606
[13, 240] loss: 0.617
[13, 300] loss: 0.617
[13, 360] loss: 0.624
Epoch: 13 -> Loss: 0.530610501766
Epoch: 13 -> Test Accuracy: 78.81
[14, 60] loss: 0.568
[14, 120] loss: 0.587
[14, 180] loss: 0.621
[14, 240] loss: 0.614
[14, 300] loss: 0.617
[14, 360] loss: 0.612
Epoch: 14 -> Loss: 0.864044070244
Epoch: 14 -> Test Accuracy: 78.74
[15, 60] loss: 0.597
[15, 120] loss: 0.594
[15, 180] loss: 0.586
[15, 240] loss: 0.598
[15, 300] loss: 0.578
[15, 360] loss: 0.624
Epoch: 15 -> Loss: 0.784435331821
Epoch: 15 -> Test Accuracy: 78.56
[16, 60] loss: 0.594
[16, 120] loss: 0.591
[16, 180] loss: 0.584
[16, 240] loss: 0.606
[16, 300] loss: 0.626
[16, 360] loss: 0.591
Epoch: 16 -> Loss: 0.565725684166
Epoch: 16 -> Test Accuracy: 78.61
[17, 60] loss: 0.573
[17, 120] loss: 0.581
[17, 180] loss: 0.611
[17, 240] loss: 0.597
[17, 300] loss: 0.590
[17, 360] loss: 0.621
Epoch: 17 -> Loss: 0.595977246761
Epoch: 17 -> Test Accuracy: 78.18
[18, 60] loss: 0.570
[18, 120] loss: 0.588
[18, 180] loss: 0.570
[18, 240] loss: 0.595
[18, 300] loss: 0.614
[18, 360] loss: 0.604
Epoch: 18 -> Loss: 0.599996745586
Epoch: 18 -> Test Accuracy: 78.87
[19, 60] loss: 0.558
[19, 120] loss: 0.578
[19, 180] loss: 0.579
[19, 240] loss: 0.604
[19, 300] loss: 0.611
[19, 360] loss: 0.589
Epoch: 19 -> Loss: 0.615851283073
Epoch: 19 -> Test Accuracy: 78.81
[20, 60] loss: 0.558
[20, 120] loss: 0.579
[20, 180] loss: 0.583
[20, 240] loss: 0.590
[20, 300] loss: 0.596
[20, 360] loss: 0.610
Epoch: 20 -> Loss: 0.605484127998
Epoch: 20 -> Test Accuracy: 79.39
[21, 60] loss: 0.538
[21, 120] loss: 0.491
[21, 180] loss: 0.480
[21, 240] loss: 0.500
[21, 300] loss: 0.482
[21, 360] loss: 0.483
Epoch: 21 -> Loss: 0.56816971302
Epoch: 21 -> Test Accuracy: 81.5
[22, 60] loss: 0.441
[22, 120] loss: 0.454
[22, 180] loss: 0.459
[22, 240] loss: 0.450
[22, 300] loss: 0.442
[22, 360] loss: 0.451
Epoch: 22 -> Loss: 0.40216255188
Epoch: 22 -> Test Accuracy: 81.47
[23, 60] loss: 0.421
[23, 120] loss: 0.454
[23, 180] loss: 0.422
[23, 240] loss: 0.433
[23, 300] loss: 0.426
[23, 360] loss: 0.438
Epoch: 23 -> Loss: 0.486105382442
Epoch: 23 -> Test Accuracy: 82.19
[24, 60] loss: 0.420
[24, 120] loss: 0.432
[24, 180] loss: 0.421
[24, 240] loss: 0.428
[24, 300] loss: 0.409
[24, 360] loss: 0.417
Epoch: 24 -> Loss: 0.365364342928
Epoch: 24 -> Test Accuracy: 82.21
[25, 60] loss: 0.403
[25, 120] loss: 0.411
[25, 180] loss: 0.400
[25, 240] loss: 0.418
[25, 300] loss: 0.423
[25, 360] loss: 0.421
Epoch: 25 -> Loss: 0.491146504879
Epoch: 25 -> Test Accuracy: 82.11
[26, 60] loss: 0.414
[26, 120] loss: 0.407
[26, 180] loss: 0.422
[26, 240] loss: 0.406
[26, 300] loss: 0.424
[26, 360] loss: 0.397
Epoch: 26 -> Loss: 0.56111395359
Epoch: 26 -> Test Accuracy: 82.16
[27, 60] loss: 0.391
[27, 120] loss: 0.405
[27, 180] loss: 0.395
[27, 240] loss: 0.395
[27, 300] loss: 0.404
[27, 360] loss: 0.401
Epoch: 27 -> Loss: 0.438163995743
Epoch: 27 -> Test Accuracy: 82.31
[28, 60] loss: 0.393
[28, 120] loss: 0.385
[28, 180] loss: 0.393
[28, 240] loss: 0.382
[28, 300] loss: 0.408
[28, 360] loss: 0.407
Epoch: 28 -> Loss: 0.282507956028
Epoch: 28 -> Test Accuracy: 82.44
[29, 60] loss: 0.390
[29, 120] loss: 0.374
[29, 180] loss: 0.378
[29, 240] loss: 0.403
[29, 300] loss: 0.394
[29, 360] loss: 0.404
Epoch: 29 -> Loss: 0.281484305859
Epoch: 29 -> Test Accuracy: 82.13
[30, 60] loss: 0.381
[30, 120] loss: 0.370
[30, 180] loss: 0.401
[30, 240] loss: 0.381
[30, 300] loss: 0.384
[30, 360] loss: 0.404
Epoch: 30 -> Loss: 0.277964651585
Epoch: 30 -> Test Accuracy: 81.73
[31, 60] loss: 0.382
[31, 120] loss: 0.378
[31, 180] loss: 0.388
[31, 240] loss: 0.387
[31, 300] loss: 0.392
[31, 360] loss: 0.378
Epoch: 31 -> Loss: 0.347091972828
Epoch: 31 -> Test Accuracy: 82.47
[32, 60] loss: 0.390
[32, 120] loss: 0.366
[32, 180] loss: 0.377
[32, 240] loss: 0.386
[32, 300] loss: 0.382
[32, 360] loss: 0.396
Epoch: 32 -> Loss: 0.42511588335
Epoch: 32 -> Test Accuracy: 81.95
[33, 60] loss: 0.371
[33, 120] loss: 0.368
[33, 180] loss: 0.377
[33, 240] loss: 0.384
[33, 300] loss: 0.388
[33, 360] loss: 0.378
Epoch: 33 -> Loss: 0.391119420528
Epoch: 33 -> Test Accuracy: 81.62
[34, 60] loss: 0.377
[34, 120] loss: 0.355
[34, 180] loss: 0.373
[34, 240] loss: 0.394
[34, 300] loss: 0.390
[34, 360] loss: 0.394
Epoch: 34 -> Loss: 0.403971672058
Epoch: 34 -> Test Accuracy: 82.03
[35, 60] loss: 0.379
[35, 120] loss: 0.371
[35, 180] loss: 0.377
[35, 240] loss: 0.385
[35, 300] loss: 0.393
[35, 360] loss: 0.386
Epoch: 35 -> Loss: 0.37564048171
Epoch: 35 -> Test Accuracy: 82.11
[36, 60] loss: 0.372
[36, 120] loss: 0.369
[36, 180] loss: 0.385
[36, 240] loss: 0.383
[36, 300] loss: 0.367
[36, 360] loss: 0.384
Epoch: 36 -> Loss: 0.331483066082
Epoch: 36 -> Test Accuracy: 81.55
[37, 60] loss: 0.365
[37, 120] loss: 0.376
[37, 180] loss: 0.365
[37, 240] loss: 0.372
[37, 300] loss: 0.384
[37, 360] loss: 0.388
Epoch: 37 -> Loss: 0.326968252659
Epoch: 37 -> Test Accuracy: 82.09
[38, 60] loss: 0.372
[38, 120] loss: 0.373
[38, 180] loss: 0.386
[38, 240] loss: 0.368
[38, 300] loss: 0.375
[38, 360] loss: 0.385
Epoch: 38 -> Loss: 0.287506878376
Epoch: 38 -> Test Accuracy: 82.01
[39, 60] loss: 0.361
[39, 120] loss: 0.375
[39, 180] loss: 0.358
[39, 240] loss: 0.384
[39, 300] loss: 0.375
[39, 360] loss: 0.362
Epoch: 39 -> Loss: 0.385202825069
Epoch: 39 -> Test Accuracy: 81.53
[40, 60] loss: 0.348
[40, 120] loss: 0.369
[40, 180] loss: 0.376
[40, 240] loss: 0.391
[40, 300] loss: 0.361
[40, 360] loss: 0.381
Epoch: 40 -> Loss: 0.411934942007
Epoch: 40 -> Test Accuracy: 82.03
[41, 60] loss: 0.352
[41, 120] loss: 0.322
[41, 180] loss: 0.322
[41, 240] loss: 0.336
[41, 300] loss: 0.333
[41, 360] loss: 0.323
Epoch: 41 -> Loss: 0.234177619219
Epoch: 41 -> Test Accuracy: 82.78
[42, 60] loss: 0.307
[42, 120] loss: 0.310
[42, 180] loss: 0.319
[42, 240] loss: 0.307
[42, 300] loss: 0.315
[42, 360] loss: 0.308
Epoch: 42 -> Loss: 0.332994103432
Epoch: 42 -> Test Accuracy: 82.73
[43, 60] loss: 0.294
[43, 120] loss: 0.293
[43, 180] loss: 0.297
[43, 240] loss: 0.304
[43, 300] loss: 0.298
[43, 360] loss: 0.299
Epoch: 43 -> Loss: 0.326020866632
Epoch: 43 -> Test Accuracy: 83.09
[44, 60] loss: 0.295
[44, 120] loss: 0.292
[44, 180] loss: 0.283
[44, 240] loss: 0.294
[44, 300] loss: 0.293
[44, 360] loss: 0.282
Epoch: 44 -> Loss: 0.404775053263
Epoch: 44 -> Test Accuracy: 82.88
[45, 60] loss: 0.272
[45, 120] loss: 0.282
[45, 180] loss: 0.285
[45, 240] loss: 0.288
[45, 300] loss: 0.278
[45, 360] loss: 0.299
Epoch: 45 -> Loss: 0.15308393538
Epoch: 45 -> Test Accuracy: 82.92
[46, 60] loss: 0.275
[46, 120] loss: 0.268
[46, 180] loss: 0.283
[46, 240] loss: 0.278
[46, 300] loss: 0.278
[46, 360] loss: 0.270
Epoch: 46 -> Loss: 0.262244164944
Epoch: 46 -> Test Accuracy: 83.05
[47, 60] loss: 0.276
[47, 120] loss: 0.267
[47, 180] loss: 0.275
[47, 240] loss: 0.279
[47, 300] loss: 0.266
[47, 360] loss: 0.262
Epoch: 47 -> Loss: 0.361472249031
Epoch: 47 -> Test Accuracy: 83.08
[48, 60] loss: 0.270
[48, 120] loss: 0.271
[48, 180] loss: 0.268
[48, 240] loss: 0.265
[48, 300] loss: 0.285
[48, 360] loss: 0.286
Epoch: 48 -> Loss: 0.355505049229
Epoch: 48 -> Test Accuracy: 83.16
[49, 60] loss: 0.272
[49, 120] loss: 0.270
[49, 180] loss: 0.271
[49, 240] loss: 0.268
[49, 300] loss: 0.257
[49, 360] loss: 0.266
Epoch: 49 -> Loss: 0.208222582936
Epoch: 49 -> Test Accuracy: 83.07
[50, 60] loss: 0.264
[50, 120] loss: 0.266
[50, 180] loss: 0.266
[50, 240] loss: 0.277
[50, 300] loss: 0.265
[50, 360] loss: 0.255
Epoch: 50 -> Loss: 0.365976423025
Epoch: 50 -> Test Accuracy: 82.99
[51, 60] loss: 0.264
[51, 120] loss: 0.262
[51, 180] loss: 0.250
[51, 240] loss: 0.259
[51, 300] loss: 0.263
[51, 360] loss: 0.264
Epoch: 51 -> Loss: 0.314839750528
Epoch: 51 -> Test Accuracy: 82.98
[52, 60] loss: 0.262
[52, 120] loss: 0.259
[52, 180] loss: 0.260
[52, 240] loss: 0.252
[52, 300] loss: 0.252
[52, 360] loss: 0.275
Epoch: 52 -> Loss: 0.206320613623
Epoch: 52 -> Test Accuracy: 83.04
[53, 60] loss: 0.267
[53, 120] loss: 0.260
[53, 180] loss: 0.267
[53, 240] loss: 0.255
[53, 300] loss: 0.267
[53, 360] loss: 0.263
Epoch: 53 -> Loss: 0.397431999445
Epoch: 53 -> Test Accuracy: 82.94
[54, 60] loss: 0.250
[54, 120] loss: 0.260
[54, 180] loss: 0.260
[54, 240] loss: 0.259
[54, 300] loss: 0.261
[54, 360] loss: 0.251
Epoch: 54 -> Loss: 0.355315774679
Epoch: 54 -> Test Accuracy: 83.27
[55, 60] loss: 0.255
[55, 120] loss: 0.270
[55, 180] loss: 0.253
[55, 240] loss: 0.236
[55, 300] loss: 0.264
[55, 360] loss: 0.265
Epoch: 55 -> Loss: 0.358705937862
Epoch: 55 -> Test Accuracy: 83.2
[56, 60] loss: 0.253
[56, 120] loss: 0.256
[56, 180] loss: 0.246
[56, 240] loss: 0.245
[56, 300] loss: 0.253
[56, 360] loss: 0.262
Epoch: 56 -> Loss: 0.272285223007
Epoch: 56 -> Test Accuracy: 83.1
[57, 60] loss: 0.252
[57, 120] loss: 0.262
[57, 180] loss: 0.251
[57, 240] loss: 0.253
[57, 300] loss: 0.249
[57, 360] loss: 0.263
Epoch: 57 -> Loss: 0.406624853611
Epoch: 57 -> Test Accuracy: 83.08
[58, 60] loss: 0.256
[58, 120] loss: 0.242
[58, 180] loss: 0.259
[58, 240] loss: 0.256
[58, 300] loss: 0.251
[58, 360] loss: 0.254
Epoch: 58 -> Loss: 0.246799662709
Epoch: 58 -> Test Accuracy: 83.04
[59, 60] loss: 0.252
[59, 120] loss: 0.276
[59, 180] loss: 0.243
[59, 240] loss: 0.263
[59, 300] loss: 0.249
[59, 360] loss: 0.257
Epoch: 59 -> Loss: 0.279025554657
Epoch: 59 -> Test Accuracy: 83.19
[60, 60] loss: 0.249
[60, 120] loss: 0.249
[60, 180] loss: 0.259
[60, 240] loss: 0.247
[60, 300] loss: 0.257
[60, 360] loss: 0.252
Epoch: 60 -> Loss: 0.197952821851
Epoch: 60 -> Test Accuracy: 83.1
[61, 60] loss: 0.253
[61, 120] loss: 0.248
[61, 180] loss: 0.253
[61, 240] loss: 0.250
[61, 300] loss: 0.253
[61, 360] loss: 0.255
Epoch: 61 -> Loss: 0.305834472179
Epoch: 61 -> Test Accuracy: 83.24
[62, 60] loss: 0.244
[62, 120] loss: 0.255
[62, 180] loss: 0.263
[62, 240] loss: 0.252
[62, 300] loss: 0.246
[62, 360] loss: 0.239
Epoch: 62 -> Loss: 0.430526345968
Epoch: 62 -> Test Accuracy: 83.12
[63, 60] loss: 0.255
[63, 120] loss: 0.251
[63, 180] loss: 0.248
[63, 240] loss: 0.235
[63, 300] loss: 0.248
[63, 360] loss: 0.258
Epoch: 63 -> Loss: 0.179082587361
Epoch: 63 -> Test Accuracy: 83.29
[64, 60] loss: 0.249
[64, 120] loss: 0.246
[64, 180] loss: 0.244
[64, 240] loss: 0.243
[64, 300] loss: 0.242
[64, 360] loss: 0.241
Epoch: 64 -> Loss: 0.266464024782
Epoch: 64 -> Test Accuracy: 83.21
[65, 60] loss: 0.243
[65, 120] loss: 0.246
[65, 180] loss: 0.247
[65, 240] loss: 0.250
[65, 300] loss: 0.249
[65, 360] loss: 0.247
Epoch: 65 -> Loss: 0.241675049067
Epoch: 65 -> Test Accuracy: 83.28
[66, 60] loss: 0.229
[66, 120] loss: 0.246
[66, 180] loss: 0.255
[66, 240] loss: 0.251
[66, 300] loss: 0.235
[66, 360] loss: 0.250
Epoch: 66 -> Loss: 0.264233797789
Epoch: 66 -> Test Accuracy: 83.12
[67, 60] loss: 0.248
[67, 120] loss: 0.244
[67, 180] loss: 0.242
[67, 240] loss: 0.238
[67, 300] loss: 0.246
[67, 360] loss: 0.243
Epoch: 67 -> Loss: 0.289677709341
Epoch: 67 -> Test Accuracy: 83.22
[68, 60] loss: 0.256
[68, 120] loss: 0.247
[68, 180] loss: 0.243
[68, 240] loss: 0.241
[68, 300] loss: 0.239
[68, 360] loss: 0.234
Epoch: 68 -> Loss: 0.230375498533
Epoch: 68 -> Test Accuracy: 83.08
[69, 60] loss: 0.234
[69, 120] loss: 0.239
[69, 180] loss: 0.242
[69, 240] loss: 0.251
[69, 300] loss: 0.233
[69, 360] loss: 0.250
Epoch: 69 -> Loss: 0.277349829674
Epoch: 69 -> Test Accuracy: 83.27
[70, 60] loss: 0.244
[70, 120] loss: 0.243
[70, 180] loss: 0.244
[70, 240] loss: 0.245
[70, 300] loss: 0.246
[70, 360] loss: 0.237
Epoch: 70 -> Loss: 0.296507179737
Epoch: 70 -> Test Accuracy: 83.14
[71, 60] loss: 0.240
[71, 120] loss: 0.230
[71, 180] loss: 0.244
[71, 240] loss: 0.235
[71, 300] loss: 0.245
[71, 360] loss: 0.230
Epoch: 71 -> Loss: 0.157645359635
Epoch: 71 -> Test Accuracy: 83.04
[72, 60] loss: 0.242
[72, 120] loss: 0.229
[72, 180] loss: 0.225
[72, 240] loss: 0.238
[72, 300] loss: 0.250
[72, 360] loss: 0.234
Epoch: 72 -> Loss: 0.279527008533
Epoch: 72 -> Test Accuracy: 83.14
[73, 60] loss: 0.234
[73, 120] loss: 0.236
[73, 180] loss: 0.227
[73, 240] loss: 0.246
[73, 300] loss: 0.242
[73, 360] loss: 0.234
Epoch: 73 -> Loss: 0.251714527607
Epoch: 73 -> Test Accuracy: 83.15
[74, 60] loss: 0.231
[74, 120] loss: 0.244
[74, 180] loss: 0.247
[74, 240] loss: 0.236
[74, 300] loss: 0.242
[74, 360] loss: 0.246
Epoch: 74 -> Loss: 0.219133019447
Epoch: 74 -> Test Accuracy: 83.16
[75, 60] loss: 0.227
[75, 120] loss: 0.242
[75, 180] loss: 0.238
[75, 240] loss: 0.234
[75, 300] loss: 0.241
[75, 360] loss: 0.244
Epoch: 75 -> Loss: 0.160952612758
Epoch: 75 -> Test Accuracy: 83.21
[76, 60] loss: 0.234
[76, 120] loss: 0.231
[76, 180] loss: 0.234
[76, 240] loss: 0.241
[76, 300] loss: 0.233
[76, 360] loss: 0.233
Epoch: 76 -> Loss: 0.194017440081
Epoch: 76 -> Test Accuracy: 83.21
[77, 60] loss: 0.225
[77, 120] loss: 0.231
[77, 180] loss: 0.235
[77, 240] loss: 0.233
[77, 300] loss: 0.233
[77, 360] loss: 0.232
Epoch: 77 -> Loss: 0.230169251561
Epoch: 77 -> Test Accuracy: 83.19
[78, 60] loss: 0.231
[78, 120] loss: 0.229
[78, 180] loss: 0.233
[78, 240] loss: 0.231
[78, 300] loss: 0.236
[78, 360] loss: 0.225
Epoch: 78 -> Loss: 0.356003493071
Epoch: 78 -> Test Accuracy: 83.28
[79, 60] loss: 0.235
[79, 120] loss: 0.230
[79, 180] loss: 0.221
[79, 240] loss: 0.235
[79, 300] loss: 0.230
[79, 360] loss: 0.237
Epoch: 79 -> Loss: 0.526965022087
Epoch: 79 -> Test Accuracy: 83.32
[80, 60] loss: 0.236
[80, 120] loss: 0.234
[80, 180] loss: 0.222
[80, 240] loss: 0.233
[80, 300] loss: 0.230
[80, 360] loss: 0.227
Epoch: 80 -> Loss: 0.198395565152
Epoch: 80 -> Test Accuracy: 83.23
[81, 60] loss: 0.223
[81, 120] loss: 0.233
[81, 180] loss: 0.238
[81, 240] loss: 0.228
[81, 300] loss: 0.224
[81, 360] loss: 0.228
Epoch: 81 -> Loss: 0.368765324354
Epoch: 81 -> Test Accuracy: 83.18
[82, 60] loss: 0.227
[82, 120] loss: 0.234
[82, 180] loss: 0.230
[82, 240] loss: 0.237
[82, 300] loss: 0.225
[82, 360] loss: 0.226
Epoch: 82 -> Loss: 0.178824096918
Epoch: 82 -> Test Accuracy: 83.09
[83, 60] loss: 0.230
[83, 120] loss: 0.235
[83, 180] loss: 0.228
[83, 240] loss: 0.231
[83, 300] loss: 0.228
[83, 360] loss: 0.235
Epoch: 83 -> Loss: 0.249970510602
Epoch: 83 -> Test Accuracy: 83.29
[84, 60] loss: 0.230
[84, 120] loss: 0.223
[84, 180] loss: 0.220
[84, 240] loss: 0.232
[84, 300] loss: 0.232
[84, 360] loss: 0.223
Epoch: 84 -> Loss: 0.275760382414
Epoch: 84 -> Test Accuracy: 83.1
[85, 60] loss: 0.224
[85, 120] loss: 0.223
[85, 180] loss: 0.217
[85, 240] loss: 0.229
[85, 300] loss: 0.232
[85, 360] loss: 0.239
Epoch: 85 -> Loss: 0.303332418203
Epoch: 85 -> Test Accuracy: 83.29
[86, 60] loss: 0.220
[86, 120] loss: 0.225
[86, 180] loss: 0.224
[86, 240] loss: 0.231
[86, 300] loss: 0.228
[86, 360] loss: 0.229
Epoch: 86 -> Loss: 0.361538261175
Epoch: 86 -> Test Accuracy: 83.19
[87, 60] loss: 0.221
[87, 120] loss: 0.224
[87, 180] loss: 0.223
[87, 240] loss: 0.225
[87, 300] loss: 0.224
[87, 360] loss: 0.215
Epoch: 87 -> Loss: 0.120859101415
Epoch: 87 -> Test Accuracy: 83.2
[88, 60] loss: 0.234
[88, 120] loss: 0.215
[88, 180] loss: 0.226
[88, 240] loss: 0.217
[88, 300] loss: 0.228
[88, 360] loss: 0.231
Epoch: 88 -> Loss: 0.202250763774
Epoch: 88 -> Test Accuracy: 83.17
[89, 60] loss: 0.220
[89, 120] loss: 0.219
[89, 180] loss: 0.231
[89, 240] loss: 0.234
[89, 300] loss: 0.223
[89, 360] loss: 0.226
Epoch: 89 -> Loss: 0.243361517787
Epoch: 89 -> Test Accuracy: 83.33
[90, 60] loss: 0.227
[90, 120] loss: 0.202
[90, 180] loss: 0.225
[90, 240] loss: 0.222
[90, 300] loss: 0.221
[90, 360] loss: 0.217
Epoch: 90 -> Loss: 0.182889983058
Epoch: 90 -> Test Accuracy: 83.38
[91, 60] loss: 0.224
[91, 120] loss: 0.221
[91, 180] loss: 0.222
[91, 240] loss: 0.217
[91, 300] loss: 0.221
[91, 360] loss: 0.235
Epoch: 91 -> Loss: 0.237830594182
Epoch: 91 -> Test Accuracy: 83.29
[92, 60] loss: 0.223
[92, 120] loss: 0.225
[92, 180] loss: 0.212
[92, 240] loss: 0.226
[92, 300] loss: 0.216
[92, 360] loss: 0.229
Epoch: 92 -> Loss: 0.118292652071
Epoch: 92 -> Test Accuracy: 83.4
[93, 60] loss: 0.218
[93, 120] loss: 0.222
[93, 180] loss: 0.212
[93, 240] loss: 0.215
[93, 300] loss: 0.228
[93, 360] loss: 0.216
Epoch: 93 -> Loss: 0.167962044477
Epoch: 93 -> Test Accuracy: 83.36
[94, 60] loss: 0.222
[94, 120] loss: 0.211
[94, 180] loss: 0.213
[94, 240] loss: 0.221
[94, 300] loss: 0.217
[94, 360] loss: 0.229
Epoch: 94 -> Loss: 0.265777915716
Epoch: 94 -> Test Accuracy: 83.24
[95, 60] loss: 0.226
[95, 120] loss: 0.206
[95, 180] loss: 0.213
[95, 240] loss: 0.223
[95, 300] loss: 0.214
[95, 360] loss: 0.221
Epoch: 95 -> Loss: 0.231031134725
Epoch: 95 -> Test Accuracy: 83.35
[96, 60] loss: 0.216
[96, 120] loss: 0.219
[96, 180] loss: 0.221
[96, 240] loss: 0.214
[96, 300] loss: 0.219
[96, 360] loss: 0.219
Epoch: 96 -> Loss: 0.162611573935
Epoch: 96 -> Test Accuracy: 83.43
[97, 60] loss: 0.216
[97, 120] loss: 0.224
[97, 180] loss: 0.215
[97, 240] loss: 0.217
[97, 300] loss: 0.215
[97, 360] loss: 0.212
Epoch: 97 -> Loss: 0.159254923463
Epoch: 97 -> Test Accuracy: 83.26
[98, 60] loss: 0.206
[98, 120] loss: 0.212
[98, 180] loss: 0.214
[98, 240] loss: 0.216
[98, 300] loss: 0.223
[98, 360] loss: 0.207
Epoch: 98 -> Loss: 0.2572067976
Epoch: 98 -> Test Accuracy: 83.34
[99, 60] loss: 0.210
[99, 120] loss: 0.203
[99, 180] loss: 0.217
[99, 240] loss: 0.219
[99, 300] loss: 0.218
[99, 360] loss: 0.214
Epoch: 99 -> Loss: 0.2847032547
Epoch: 99 -> Test Accuracy: 83.26
[100, 60] loss: 0.207
[100, 120] loss: 0.225
[100, 180] loss: 0.211
[100, 240] loss: 0.217
[100, 300] loss: 0.219
[100, 360] loss: 0.223
Epoch: 100 -> Loss: 0.204636290669
Epoch: 100 -> Test Accuracy: 83.28
Finished Training
[1, 60] loss: 1.644
[1, 120] loss: 0.821
[1, 180] loss: 0.744
[1, 240] loss: 0.710
[1, 300] loss: 0.679
[1, 360] loss: 0.649
Epoch: 1 -> Loss: 0.701272428036
Epoch: 1 -> Test Accuracy: 78.65
[2, 60] loss: 0.597
[2, 120] loss: 0.585
[2, 180] loss: 0.572
[2, 240] loss: 0.545
[2, 300] loss: 0.565
[2, 360] loss: 0.560
Epoch: 2 -> Loss: 0.561306715012
Epoch: 2 -> Test Accuracy: 80.37
[3, 60] loss: 0.513
[3, 120] loss: 0.520
[3, 180] loss: 0.511
[3, 240] loss: 0.508
[3, 300] loss: 0.510
[3, 360] loss: 0.508
Epoch: 3 -> Loss: 0.586759090424
Epoch: 3 -> Test Accuracy: 81.28
[4, 60] loss: 0.494
[4, 120] loss: 0.475
[4, 180] loss: 0.473
[4, 240] loss: 0.491
[4, 300] loss: 0.462
[4, 360] loss: 0.490
Epoch: 4 -> Loss: 0.434667438269
Epoch: 4 -> Test Accuracy: 81.58
[5, 60] loss: 0.442
[5, 120] loss: 0.460
[5, 180] loss: 0.451
[5, 240] loss: 0.461
[5, 300] loss: 0.440
[5, 360] loss: 0.465
Epoch: 5 -> Loss: 0.457592815161
Epoch: 5 -> Test Accuracy: 81.89
[6, 60] loss: 0.423
[6, 120] loss: 0.434
[6, 180] loss: 0.416
[6, 240] loss: 0.449
[6, 300] loss: 0.448
[6, 360] loss: 0.448
Epoch: 6 -> Loss: 0.407326281071
Epoch: 6 -> Test Accuracy: 83.07
[7, 60] loss: 0.428
[7, 120] loss: 0.404
[7, 180] loss: 0.411
[7, 240] loss: 0.436
[7, 300] loss: 0.428
[7, 360] loss: 0.439
Epoch: 7 -> Loss: 0.390617400408
Epoch: 7 -> Test Accuracy: 82.82
[8, 60] loss: 0.395
[8, 120] loss: 0.398
[8, 180] loss: 0.412
[8, 240] loss: 0.411
[8, 300] loss: 0.425
[8, 360] loss: 0.438
Epoch: 8 -> Loss: 0.387452274561
Epoch: 8 -> Test Accuracy: 83.57
[9, 60] loss: 0.402
[9, 120] loss: 0.409
[9, 180] loss: 0.389
[9, 240] loss: 0.402
[9, 300] loss: 0.417
[9, 360] loss: 0.426
Epoch: 9 -> Loss: 0.486477464437
Epoch: 9 -> Test Accuracy: 82.97
[10, 60] loss: 0.388
[10, 120] loss: 0.388
[10, 180] loss: 0.393
[10, 240] loss: 0.412
[10, 300] loss: 0.391
[10, 360] loss: 0.421
Epoch: 10 -> Loss: 0.426252067089
Epoch: 10 -> Test Accuracy: 84.33
[11, 60] loss: 0.380
[11, 120] loss: 0.385
[11, 180] loss: 0.388
[11, 240] loss: 0.404
[11, 300] loss: 0.397
[11, 360] loss: 0.404
Epoch: 11 -> Loss: 0.693639338017
Epoch: 11 -> Test Accuracy: 83.25
[12, 60] loss: 0.362
[12, 120] loss: 0.360
[12, 180] loss: 0.378
[12, 240] loss: 0.404
[12, 300] loss: 0.396
[12, 360] loss: 0.400
Epoch: 12 -> Loss: 0.53497749567
Epoch: 12 -> Test Accuracy: 83.23
[13, 60] loss: 0.369
[13, 120] loss: 0.372
[13, 180] loss: 0.364
[13, 240] loss: 0.381
[13, 300] loss: 0.404
[13, 360] loss: 0.414
Epoch: 13 -> Loss: 0.437541097403
Epoch: 13 -> Test Accuracy: 84.03
[14, 60] loss: 0.378
[14, 120] loss: 0.374
[14, 180] loss: 0.376
[14, 240] loss: 0.391
[14, 300] loss: 0.373
[14, 360] loss: 0.397
Epoch: 14 -> Loss: 0.38378995657
Epoch: 14 -> Test Accuracy: 83.58
[15, 60] loss: 0.370
[15, 120] loss: 0.372
[15, 180] loss: 0.377
[15, 240] loss: 0.386
[15, 300] loss: 0.364
[15, 360] loss: 0.381
Epoch: 15 -> Loss: 0.310917705297
Epoch: 15 -> Test Accuracy: 83.59
[16, 60] loss: 0.371
[16, 120] loss: 0.357
[16, 180] loss: 0.366
[16, 240] loss: 0.375
[16, 300] loss: 0.372
[16, 360] loss: 0.393
Epoch: 16 -> Loss: 0.276065915823
Epoch: 16 -> Test Accuracy: 83.46
[17, 60] loss: 0.357
[17, 120] loss: 0.356
[17, 180] loss: 0.381
[17, 240] loss: 0.373
[17, 300] loss: 0.382
[17, 360] loss: 0.379
Epoch: 17 -> Loss: 0.250696003437
Epoch: 17 -> Test Accuracy: 83.86
[18, 60] loss: 0.358
[18, 120] loss: 0.374
[18, 180] loss: 0.379
[18, 240] loss: 0.371
[18, 300] loss: 0.387
[18, 360] loss: 0.371
Epoch: 18 -> Loss: 0.264555454254
Epoch: 18 -> Test Accuracy: 83.73
[19, 60] loss: 0.344
[19, 120] loss: 0.359
[19, 180] loss: 0.371
[19, 240] loss: 0.373
[19, 300] loss: 0.379
[19, 360] loss: 0.382
Epoch: 19 -> Loss: 0.384335100651
Epoch: 19 -> Test Accuracy: 84.08
[20, 60] loss: 0.340
[20, 120] loss: 0.364
[20, 180] loss: 0.350
[20, 240] loss: 0.376
[20, 300] loss: 0.377
[20, 360] loss: 0.365
Epoch: 20 -> Loss: 0.480647653341
Epoch: 20 -> Test Accuracy: 83.36
[21, 60] loss: 0.303
[21, 120] loss: 0.312
[21, 180] loss: 0.289
[21, 240] loss: 0.288
[21, 300] loss: 0.295
[21, 360] loss: 0.273
Epoch: 21 -> Loss: 0.234928324819
Epoch: 21 -> Test Accuracy: 85.6
[22, 60] loss: 0.270
[22, 120] loss: 0.271
[22, 180] loss: 0.268
[22, 240] loss: 0.258
[22, 300] loss: 0.254
[22, 360] loss: 0.256
Epoch: 22 -> Loss: 0.315085470676
Epoch: 22 -> Test Accuracy: 85.78
[23, 60] loss: 0.243
[23, 120] loss: 0.246
[23, 180] loss: 0.241
[23, 240] loss: 0.244
[23, 300] loss: 0.248
[23, 360] loss: 0.240
Epoch: 23 -> Loss: 0.207869812846
Epoch: 23 -> Test Accuracy: 85.97
[24, 60] loss: 0.223
[24, 120] loss: 0.228
[24, 180] loss: 0.231
[24, 240] loss: 0.219
[24, 300] loss: 0.237
[24, 360] loss: 0.247
Epoch: 24 -> Loss: 0.22454893589
Epoch: 24 -> Test Accuracy: 86.11
[25, 60] loss: 0.215
[25, 120] loss: 0.224
[25, 180] loss: 0.227
[25, 240] loss: 0.216
[25, 300] loss: 0.225
[25, 360] loss: 0.228
Epoch: 25 -> Loss: 0.151886552572
Epoch: 25 -> Test Accuracy: 86.24
[26, 60] loss: 0.218
[26, 120] loss: 0.219
[26, 180] loss: 0.215
[26, 240] loss: 0.233
[26, 300] loss: 0.231
[26, 360] loss: 0.212
Epoch: 26 -> Loss: 0.146667078137
Epoch: 26 -> Test Accuracy: 86.05
[27, 60] loss: 0.197
[27, 120] loss: 0.212
[27, 180] loss: 0.211
[27, 240] loss: 0.213
[27, 300] loss: 0.216
[27, 360] loss: 0.221
Epoch: 27 -> Loss: 0.311142802238
Epoch: 27 -> Test Accuracy: 85.84
[28, 60] loss: 0.195
[28, 120] loss: 0.207
[28, 180] loss: 0.207
[28, 240] loss: 0.218
[28, 300] loss: 0.200
[28, 360] loss: 0.209
Epoch: 28 -> Loss: 0.142397254705
Epoch: 28 -> Test Accuracy: 85.58
[29, 60] loss: 0.192
[29, 120] loss: 0.209
[29, 180] loss: 0.199
[29, 240] loss: 0.206
[29, 300] loss: 0.199
[29, 360] loss: 0.204
Epoch: 29 -> Loss: 0.286255061626
Epoch: 29 -> Test Accuracy: 85.57
[30, 60] loss: 0.196
[30, 120] loss: 0.197
[30, 180] loss: 0.198
[30, 240] loss: 0.204
[30, 300] loss: 0.196
[30, 360] loss: 0.205
Epoch: 30 -> Loss: 0.151985600591
Epoch: 30 -> Test Accuracy: 85.61
[31, 60] loss: 0.185
[31, 120] loss: 0.205
[31, 180] loss: 0.200
[31, 240] loss: 0.197
[31, 300] loss: 0.200
[31, 360] loss: 0.219
Epoch: 31 -> Loss: 0.199593648314
Epoch: 31 -> Test Accuracy: 85.99
[32, 60] loss: 0.192
[32, 120] loss: 0.210
[32, 180] loss: 0.209
[32, 240] loss: 0.206
[32, 300] loss: 0.199
[32, 360] loss: 0.209
Epoch: 32 -> Loss: 0.191033646464
Epoch: 32 -> Test Accuracy: 85.83
[33, 60] loss: 0.194
[33, 120] loss: 0.191
[33, 180] loss: 0.190
[33, 240] loss: 0.189
[33, 300] loss: 0.195
[33, 360] loss: 0.201
Epoch: 33 -> Loss: 0.201527312398
Epoch: 33 -> Test Accuracy: 85.09
[34, 60] loss: 0.186
[34, 120] loss: 0.198
[34, 180] loss: 0.195
[34, 240] loss: 0.194
[34, 300] loss: 0.208
[34, 360] loss: 0.210
Epoch: 34 -> Loss: 0.233592748642
Epoch: 34 -> Test Accuracy: 85.53
[35, 60] loss: 0.181
[35, 120] loss: 0.179
[35, 180] loss: 0.187
[35, 240] loss: 0.201
[35, 300] loss: 0.206
[35, 360] loss: 0.201
Epoch: 35 -> Loss: 0.152111202478
Epoch: 35 -> Test Accuracy: 85.55
[36, 60] loss: 0.195
[36, 120] loss: 0.195
[36, 180] loss: 0.190
[36, 240] loss: 0.200
[36, 300] loss: 0.198
[36, 360] loss: 0.214
Epoch: 36 -> Loss: 0.131010040641
Epoch: 36 -> Test Accuracy: 85.71
[37, 60] loss: 0.189
[37, 120] loss: 0.182
[37, 180] loss: 0.182
[37, 240] loss: 0.190
[37, 300] loss: 0.198
[37, 360] loss: 0.203
Epoch: 37 -> Loss: 0.173107802868
Epoch: 37 -> Test Accuracy: 85.54
[38, 60] loss: 0.185
[38, 120] loss: 0.193
[38, 180] loss: 0.193
[38, 240] loss: 0.193
[38, 300] loss: 0.188
[38, 360] loss: 0.186
Epoch: 38 -> Loss: 0.176717355847
Epoch: 38 -> Test Accuracy: 85.57
[39, 60] loss: 0.180
[39, 120] loss: 0.182
[39, 180] loss: 0.195
[39, 240] loss: 0.185
[39, 300] loss: 0.198
[39, 360] loss: 0.210
Epoch: 39 -> Loss: 0.266058504581
Epoch: 39 -> Test Accuracy: 85.68
[40, 60] loss: 0.176
[40, 120] loss: 0.173
[40, 180] loss: 0.196
[40, 240] loss: 0.192
[40, 300] loss: 0.194
[40, 360] loss: 0.197
Epoch: 40 -> Loss: 0.314575463533
Epoch: 40 -> Test Accuracy: 85.45
[41, 60] loss: 0.163
[41, 120] loss: 0.159
[41, 180] loss: 0.164
[41, 240] loss: 0.152
[41, 300] loss: 0.157
[41, 360] loss: 0.145
Epoch: 41 -> Loss: 0.162493079901
Epoch: 41 -> Test Accuracy: 85.91
[42, 60] loss: 0.142
[42, 120] loss: 0.137
[42, 180] loss: 0.145
[42, 240] loss: 0.141
[42, 300] loss: 0.145
[42, 360] loss: 0.139
Epoch: 42 -> Loss: 0.162816256285
Epoch: 42 -> Test Accuracy: 86.22
[43, 60] loss: 0.125
[43, 120] loss: 0.131
[43, 180] loss: 0.131
[43, 240] loss: 0.138
[43, 300] loss: 0.141
[43, 360] loss: 0.142
Epoch: 43 -> Loss: 0.157318085432
Epoch: 43 -> Test Accuracy: 86.57
[44, 60] loss: 0.124
[44, 120] loss: 0.121
[44, 180] loss: 0.129
[44, 240] loss: 0.127
[44, 300] loss: 0.138
[44, 360] loss: 0.129
Epoch: 44 -> Loss: 0.0897534042597
Epoch: 44 -> Test Accuracy: 86.6
[45, 60] loss: 0.131
[45, 120] loss: 0.122
[45, 180] loss: 0.126
[45, 240] loss: 0.119
[45, 300] loss: 0.134
[45, 360] loss: 0.127
Epoch: 45 -> Loss: 0.0621313527226
Epoch: 45 -> Test Accuracy: 86.34
[46, 60] loss: 0.123
[46, 120] loss: 0.111
[46, 180] loss: 0.116
[46, 240] loss: 0.116
[46, 300] loss: 0.119
[46, 360] loss: 0.122
Epoch: 46 -> Loss: 0.121094107628
Epoch: 46 -> Test Accuracy: 86.45
[47, 60] loss: 0.116
[47, 120] loss: 0.119
[47, 180] loss: 0.114
[47, 240] loss: 0.115
[47, 300] loss: 0.116
[47, 360] loss: 0.115
Epoch: 47 -> Loss: 0.111577153206
Epoch: 47 -> Test Accuracy: 86.57
[48, 60] loss: 0.120
[48, 120] loss: 0.116
[48, 180] loss: 0.112
[48, 240] loss: 0.111
[48, 300] loss: 0.111
[48, 360] loss: 0.108
Epoch: 48 -> Loss: 0.214693546295
Epoch: 48 -> Test Accuracy: 86.52
[49, 60] loss: 0.105
[49, 120] loss: 0.112
[49, 180] loss: 0.111
[49, 240] loss: 0.111
[49, 300] loss: 0.114
[49, 360] loss: 0.110
Epoch: 49 -> Loss: 0.0762288421392
Epoch: 49 -> Test Accuracy: 86.5
[50, 60] loss: 0.110
[50, 120] loss: 0.109
[50, 180] loss: 0.112
[50, 240] loss: 0.114
[50, 300] loss: 0.107
[50, 360] loss: 0.112
Epoch: 50 -> Loss: 0.135717004538
Epoch: 50 -> Test Accuracy: 86.55
[51, 60] loss: 0.108
[51, 120] loss: 0.114
[51, 180] loss: 0.107
[51, 240] loss: 0.106
[51, 300] loss: 0.112
[51, 360] loss: 0.112
Epoch: 51 -> Loss: 0.0814069435
Epoch: 51 -> Test Accuracy: 86.54
[52, 60] loss: 0.111
[52, 120] loss: 0.111
[52, 180] loss: 0.107
[52, 240] loss: 0.108
[52, 300] loss: 0.108
[52, 360] loss: 0.114
Epoch: 52 -> Loss: 0.183041721582
Epoch: 52 -> Test Accuracy: 86.56
[53, 60] loss: 0.105
[53, 120] loss: 0.110
[53, 180] loss: 0.100
[53, 240] loss: 0.111
[53, 300] loss: 0.098
[53, 360] loss: 0.107
Epoch: 53 -> Loss: 0.111024282873
Epoch: 53 -> Test Accuracy: 86.5
[54, 60] loss: 0.102
[54, 120] loss: 0.109
[54, 180] loss: 0.099
[54, 240] loss: 0.106
[54, 300] loss: 0.105
[54, 360] loss: 0.109
Epoch: 54 -> Loss: 0.17025090754
Epoch: 54 -> Test Accuracy: 86.5
[55, 60] loss: 0.107
[55, 120] loss: 0.104
[55, 180] loss: 0.106
[55, 240] loss: 0.106
[55, 300] loss: 0.105
[55, 360] loss: 0.105
Epoch: 55 -> Loss: 0.123021617532
Epoch: 55 -> Test Accuracy: 86.49
[56, 60] loss: 0.099
[56, 120] loss: 0.101
[56, 180] loss: 0.105
[56, 240] loss: 0.100
[56, 300] loss: 0.105
[56, 360] loss: 0.102
Epoch: 56 -> Loss: 0.0429081134498
Epoch: 56 -> Test Accuracy: 86.66
[57, 60] loss: 0.099
[57, 120] loss: 0.100
[57, 180] loss: 0.097
[57, 240] loss: 0.102
[57, 300] loss: 0.099
[57, 360] loss: 0.101
Epoch: 57 -> Loss: 0.085507825017
Epoch: 57 -> Test Accuracy: 86.43
[58, 60] loss: 0.105
[58, 120] loss: 0.102
[58, 180] loss: 0.107
[58, 240] loss: 0.095
[58, 300] loss: 0.094
[58, 360] loss: 0.101
Epoch: 58 -> Loss: 0.186266377568
Epoch: 58 -> Test Accuracy: 86.62
[59, 60] loss: 0.101
[59, 120] loss: 0.105
[59, 180] loss: 0.098
[59, 240] loss: 0.099
[59, 300] loss: 0.102
[59, 360] loss: 0.102
Epoch: 59 -> Loss: 0.116454288363
Epoch: 59 -> Test Accuracy: 86.42
[60, 60] loss: 0.110
[60, 120] loss: 0.096
[60, 180] loss: 0.101
[60, 240] loss: 0.101
[60, 300] loss: 0.101
[60, 360] loss: 0.101
Epoch: 60 -> Loss: 0.199208289385
Epoch: 60 -> Test Accuracy: 86.56
[61, 60] loss: 0.099
[61, 120] loss: 0.089
[61, 180] loss: 0.101
[61, 240] loss: 0.097
[61, 300] loss: 0.100
[61, 360] loss: 0.096
Epoch: 61 -> Loss: 0.229684591293
Epoch: 61 -> Test Accuracy: 86.53
[62, 60] loss: 0.093
[62, 120] loss: 0.100
[62, 180] loss: 0.096
[62, 240] loss: 0.091
[62, 300] loss: 0.105
[62, 360] loss: 0.101
Epoch: 62 -> Loss: 0.0992732420564
Epoch: 62 -> Test Accuracy: 86.34
[63, 60] loss: 0.098
[63, 120] loss: 0.092
[63, 180] loss: 0.096
[63, 240] loss: 0.095
[63, 300] loss: 0.097
[63, 360] loss: 0.093
Epoch: 63 -> Loss: 0.107236407697
Epoch: 63 -> Test Accuracy: 86.44
[64, 60] loss: 0.093
[64, 120] loss: 0.096
[64, 180] loss: 0.097
[64, 240] loss: 0.096
[64, 300] loss: 0.095
[64, 360] loss: 0.099
Epoch: 64 -> Loss: 0.141364723444
Epoch: 64 -> Test Accuracy: 86.53
[65, 60] loss: 0.097
[65, 120] loss: 0.094
[65, 180] loss: 0.103
[65, 240] loss: 0.086
[65, 300] loss: 0.089
[65, 360] loss: 0.107
Epoch: 65 -> Loss: 0.11465189606
Epoch: 65 -> Test Accuracy: 86.36
[66, 60] loss: 0.099
[66, 120] loss: 0.098
[66, 180] loss: 0.092
[66, 240] loss: 0.088
[66, 300] loss: 0.097
[66, 360] loss: 0.091
Epoch: 66 -> Loss: 0.126636952162
Epoch: 66 -> Test Accuracy: 86.49
[67, 60] loss: 0.090
[67, 120] loss: 0.089
[67, 180] loss: 0.094
[67, 240] loss: 0.091
[67, 300] loss: 0.091
[67, 360] loss: 0.097
Epoch: 67 -> Loss: 0.0722390115261
Epoch: 67 -> Test Accuracy: 86.52
[68, 60] loss: 0.093
[68, 120] loss: 0.090
[68, 180] loss: 0.093
[68, 240] loss: 0.095
[68, 300] loss: 0.094
[68, 360] loss: 0.097
Epoch: 68 -> Loss: 0.0976048186421
Epoch: 68 -> Test Accuracy: 86.5
[69, 60] loss: 0.090
[69, 120] loss: 0.094
[69, 180] loss: 0.090
[69, 240] loss: 0.098
[69, 300] loss: 0.087
[69, 360] loss: 0.090
Epoch: 69 -> Loss: 0.124031342566
Epoch: 69 -> Test Accuracy: 86.45
[70, 60] loss: 0.094
[70, 120] loss: 0.088
[70, 180] loss: 0.090
[70, 240] loss: 0.087
[70, 300] loss: 0.089
[70, 360] loss: 0.091
Epoch: 70 -> Loss: 0.0749558657408
Epoch: 70 -> Test Accuracy: 86.42
[71, 60] loss: 0.092
[71, 120] loss: 0.093
[71, 180] loss: 0.086
[71, 240] loss: 0.088
[71, 300] loss: 0.084
[71, 360] loss: 0.094
Epoch: 71 -> Loss: 0.264926105738
Epoch: 71 -> Test Accuracy: 86.61
[72, 60] loss: 0.092
[72, 120] loss: 0.083
[72, 180] loss: 0.090
[72, 240] loss: 0.089
[72, 300] loss: 0.087
[72, 360] loss: 0.090
Epoch: 72 -> Loss: 0.0532714352012
Epoch: 72 -> Test Accuracy: 86.57
[73, 60] loss: 0.094
[73, 120] loss: 0.092
[73, 180] loss: 0.088
[73, 240] loss: 0.092
[73, 300] loss: 0.083
[73, 360] loss: 0.090
Epoch: 73 -> Loss: 0.0466169789433
Epoch: 73 -> Test Accuracy: 86.54
[74, 60] loss: 0.090
[74, 120] loss: 0.087
[74, 180] loss: 0.086
[74, 240] loss: 0.092
[74, 300] loss: 0.086
[74, 360] loss: 0.088
Epoch: 74 -> Loss: 0.0855236947536
Epoch: 74 -> Test Accuracy: 86.55
[75, 60] loss: 0.086
[75, 120] loss: 0.088
[75, 180] loss: 0.084
[75, 240] loss: 0.090
[75, 300] loss: 0.084
[75, 360] loss: 0.087
Epoch: 75 -> Loss: 0.0465160682797
Epoch: 75 -> Test Accuracy: 86.55
[76, 60] loss: 0.094
[76, 120] loss: 0.088
[76, 180] loss: 0.081
[76, 240] loss: 0.087
[76, 300] loss: 0.090
[76, 360] loss: 0.093
Epoch: 76 -> Loss: 0.0497290790081
Epoch: 76 -> Test Accuracy: 86.56
[77, 60] loss: 0.084
[77, 120] loss: 0.089
[77, 180] loss: 0.088
[77, 240] loss: 0.080
[77, 300] loss: 0.088
[77, 360] loss: 0.088
Epoch: 77 -> Loss: 0.0906545221806
Epoch: 77 -> Test Accuracy: 86.48
[78, 60] loss: 0.084
[78, 120] loss: 0.084
[78, 180] loss: 0.085
[78, 240] loss: 0.083
[78, 300] loss: 0.085
[78, 360] loss: 0.094
Epoch: 78 -> Loss: 0.121591173112
Epoch: 78 -> Test Accuracy: 86.69
[79, 60] loss: 0.078
[79, 120] loss: 0.084
[79, 180] loss: 0.083
[79, 240] loss: 0.087
[79, 300] loss: 0.094
[79, 360] loss: 0.086
Epoch: 79 -> Loss: 0.142673268914
Epoch: 79 -> Test Accuracy: 86.56
[80, 60] loss: 0.088
[80, 120] loss: 0.083
[80, 180] loss: 0.086
[80, 240] loss: 0.090
[80, 300] loss: 0.079
[80, 360] loss: 0.082
Epoch: 80 -> Loss: 0.0883778780699
Epoch: 80 -> Test Accuracy: 86.63
[81, 60] loss: 0.084
[81, 120] loss: 0.085
[81, 180] loss: 0.081
[81, 240] loss: 0.085
[81, 300] loss: 0.083
[81, 360] loss: 0.087
Epoch: 81 -> Loss: 0.0469513982534
Epoch: 81 -> Test Accuracy: 86.6
[82, 60] loss: 0.088
[82, 120] loss: 0.083
[82, 180] loss: 0.082
[82, 240] loss: 0.088
[82, 300] loss: 0.083
[82, 360] loss: 0.087
Epoch: 82 -> Loss: 0.0524021983147
Epoch: 82 -> Test Accuracy: 86.54
[83, 60] loss: 0.086
[83, 120] loss: 0.086
[83, 180] loss: 0.084
[83, 240] loss: 0.079
[83, 300] loss: 0.078
[83, 360] loss: 0.088
Epoch: 83 -> Loss: 0.116114482284
Epoch: 83 -> Test Accuracy: 86.54
[84, 60] loss: 0.075
[84, 120] loss: 0.085
[84, 180] loss: 0.078
[84, 240] loss: 0.078
[84, 300] loss: 0.085
[84, 360] loss: 0.081
Epoch: 84 -> Loss: 0.0494220852852
Epoch: 84 -> Test Accuracy: 86.39
[85, 60] loss: 0.085
[85, 120] loss: 0.080
[85, 180] loss: 0.085
[85, 240] loss: 0.081
[85, 300] loss: 0.085
[85, 360] loss: 0.085
Epoch: 85 -> Loss: 0.0958910509944
Epoch: 85 -> Test Accuracy: 86.6
[86, 60] loss: 0.074
[86, 120] loss: 0.083
[86, 180] loss: 0.081
[86, 240] loss: 0.083
[86, 300] loss: 0.080
[86, 360] loss: 0.086
Epoch: 86 -> Loss: 0.0433759093285
Epoch: 86 -> Test Accuracy: 86.43
[87, 60] loss: 0.079
[87, 120] loss: 0.084
[87, 180] loss: 0.083
[87, 240] loss: 0.088
[87, 300] loss: 0.080
[87, 360] loss: 0.080
Epoch: 87 -> Loss: 0.0512029603124
Epoch: 87 -> Test Accuracy: 86.61
[88, 60] loss: 0.077
[88, 120] loss: 0.082
[88, 180] loss: 0.082
[88, 240] loss: 0.079
[88, 300] loss: 0.079
[88, 360] loss: 0.081
Epoch: 88 -> Loss: 0.142117246985
Epoch: 88 -> Test Accuracy: 86.45
[89, 60] loss: 0.082
[89, 120] loss: 0.078
[89, 180] loss: 0.077
[89, 240] loss: 0.078
[89, 300] loss: 0.080
[89, 360] loss: 0.078
Epoch: 89 -> Loss: 0.0750356838107
Epoch: 89 -> Test Accuracy: 86.61
[90, 60] loss: 0.078
[90, 120] loss: 0.077
[90, 180] loss: 0.079
[90, 240] loss: 0.079
[90, 300] loss: 0.076
[90, 360] loss: 0.075
Epoch: 90 -> Loss: 0.0498711057007
Epoch: 90 -> Test Accuracy: 86.61
[91, 60] loss: 0.080
[91, 120] loss: 0.077
[91, 180] loss: 0.077
[91, 240] loss: 0.077
[91, 300] loss: 0.075
[91, 360] loss: 0.080
Epoch: 91 -> Loss: 0.189132228494
Epoch: 91 -> Test Accuracy: 86.5
[92, 60] loss: 0.075
[92, 120] loss: 0.074
[92, 180] loss: 0.075
[92, 240] loss: 0.076
[92, 300] loss: 0.085
[92, 360] loss: 0.076
Epoch: 92 -> Loss: 0.0656943097711
Epoch: 92 -> Test Accuracy: 86.42
[93, 60] loss: 0.075
[93, 120] loss: 0.071
[93, 180] loss: 0.076
[93, 240] loss: 0.076
[93, 300] loss: 0.076
[93, 360] loss: 0.075
Epoch: 93 -> Loss: 0.106649264693
Epoch: 93 -> Test Accuracy: 86.51
[94, 60] loss: 0.088
[94, 120] loss: 0.072
[94, 180] loss: 0.072
[94, 240] loss: 0.074
[94, 300] loss: 0.071
[94, 360] loss: 0.081
Epoch: 94 -> Loss: 0.119260944426
Epoch: 94 -> Test Accuracy: 86.52
[95, 60] loss: 0.076
[95, 120] loss: 0.074
[95, 180] loss: 0.078
[95, 240] loss: 0.073
[95, 300] loss: 0.076
[95, 360] loss: 0.076
Epoch: 95 -> Loss: 0.0358727164567
Epoch: 95 -> Test Accuracy: 86.48
[96, 60] loss: 0.077
[96, 120] loss: 0.069
[96, 180] loss: 0.074
[96, 240] loss: 0.079
[96, 300] loss: 0.075
[96, 360] loss: 0.071
Epoch: 96 -> Loss: 0.103197000921
Epoch: 96 -> Test Accuracy: 86.7
[97, 60] loss: 0.074
[97, 120] loss: 0.075
[97, 180] loss: 0.071
[97, 240] loss: 0.079
[97, 300] loss: 0.073
[97, 360] loss: 0.076
Epoch: 97 -> Loss: 0.157634466887
Epoch: 97 -> Test Accuracy: 86.58
[98, 60] loss: 0.078
[98, 120] loss: 0.073
[98, 180] loss: 0.066
[98, 240] loss: 0.077
[98, 300] loss: 0.078
[98, 360] loss: 0.071
Epoch: 98 -> Loss: 0.107171714306
Epoch: 98 -> Test Accuracy: 86.53
[99, 60] loss: 0.069
[99, 120] loss: 0.075
[99, 180] loss: 0.081
[99, 240] loss: 0.074
[99, 300] loss: 0.077
[99, 360] loss: 0.068
Epoch: 99 -> Loss: 0.254891574383
Epoch: 99 -> Test Accuracy: 86.6
[100, 60] loss: 0.074
[100, 120] loss: 0.073
[100, 180] loss: 0.076
[100, 240] loss: 0.077
[100, 300] loss: 0.078
[100, 360] loss: 0.078
Epoch: 100 -> Loss: 0.0800180584192
Epoch: 100 -> Test Accuracy: 86.51
Finished Training
[1, 60] loss: 2.728
[1, 120] loss: 1.803
[1, 180] loss: 1.762
[1, 240] loss: 1.721
[1, 300] loss: 1.705
[1, 360] loss: 1.687
Epoch: 1 -> Loss: 1.52180790901
Epoch: 1 -> Test Accuracy: 37.26
[2, 60] loss: 1.664
[2, 120] loss: 1.644
[2, 180] loss: 1.637
[2, 240] loss: 1.625
[2, 300] loss: 1.591
[2, 360] loss: 1.606
Epoch: 2 -> Loss: 1.53919839859
Epoch: 2 -> Test Accuracy: 40.21
[3, 60] loss: 1.565
[3, 120] loss: 1.564
[3, 180] loss: 1.590
[3, 240] loss: 1.567
[3, 300] loss: 1.561
[3, 360] loss: 1.553
Epoch: 3 -> Loss: 1.50751459599
Epoch: 3 -> Test Accuracy: 40.98
[4, 60] loss: 1.559
[4, 120] loss: 1.531
[4, 180] loss: 1.526
[4, 240] loss: 1.527
[4, 300] loss: 1.523
[4, 360] loss: 1.516
Epoch: 4 -> Loss: 1.84346234798
Epoch: 4 -> Test Accuracy: 42.62
[5, 60] loss: 1.516
[5, 120] loss: 1.522
[5, 180] loss: 1.520
[5, 240] loss: 1.507
[5, 300] loss: 1.493
[5, 360] loss: 1.504
Epoch: 5 -> Loss: 1.55798828602
Epoch: 5 -> Test Accuracy: 43.2
[6, 60] loss: 1.506
[6, 120] loss: 1.501
[6, 180] loss: 1.493
[6, 240] loss: 1.502
[6, 300] loss: 1.496
[6, 360] loss: 1.489
Epoch: 6 -> Loss: 1.55128777027
Epoch: 6 -> Test Accuracy: 44.04
[7, 60] loss: 1.484
[7, 120] loss: 1.479
[7, 180] loss: 1.480
[7, 240] loss: 1.489
[7, 300] loss: 1.490
[7, 360] loss: 1.497
Epoch: 7 -> Loss: 1.38384413719
Epoch: 7 -> Test Accuracy: 43.72
[8, 60] loss: 1.477
[8, 120] loss: 1.487
[8, 180] loss: 1.476
[8, 240] loss: 1.464
[8, 300] loss: 1.475
[8, 360] loss: 1.473
Epoch: 8 -> Loss: 1.44293642044
Epoch: 8 -> Test Accuracy: 43.53
[9, 60] loss: 1.465
[9, 120] loss: 1.465
[9, 180] loss: 1.478
[9, 240] loss: 1.463
[9, 300] loss: 1.478
[9, 360] loss: 1.462
Epoch: 9 -> Loss: 1.58194768429
Epoch: 9 -> Test Accuracy: 44.31
[10, 60] loss: 1.480
[10, 120] loss: 1.461
[10, 180] loss: 1.447
[10, 240] loss: 1.470
[10, 300] loss: 1.470
[10, 360] loss: 1.457
Epoch: 10 -> Loss: 1.58967161179
Epoch: 10 -> Test Accuracy: 44.39
[11, 60] loss: 1.453
[11, 120] loss: 1.458
[11, 180] loss: 1.471
[11, 240] loss: 1.458
[11, 300] loss: 1.453
[11, 360] loss: 1.481
Epoch: 11 -> Loss: 1.41367149353
Epoch: 11 -> Test Accuracy: 43.93
[12, 60] loss: 1.469
[12, 120] loss: 1.442
[12, 180] loss: 1.453
[12, 240] loss: 1.462
[12, 300] loss: 1.472
[12, 360] loss: 1.448
Epoch: 12 -> Loss: 1.50912034512
Epoch: 12 -> Test Accuracy: 45.51
[13, 60] loss: 1.460
[13, 120] loss: 1.454
[13, 180] loss: 1.432
[13, 240] loss: 1.463
[13, 300] loss: 1.443
[13, 360] loss: 1.472
Epoch: 13 -> Loss: 1.46690046787
Epoch: 13 -> Test Accuracy: 44.22
[14, 60] loss: 1.442
[14, 120] loss: 1.456
[14, 180] loss: 1.473
[14, 240] loss: 1.452
[14, 300] loss: 1.442
[14, 360] loss: 1.444
Epoch: 14 -> Loss: 1.42689204216
Epoch: 14 -> Test Accuracy: 44.5
[15, 60] loss: 1.440
[15, 120] loss: 1.459
[15, 180] loss: 1.451
[15, 240] loss: 1.440
[15, 300] loss: 1.465
[15, 360] loss: 1.448
Epoch: 15 -> Loss: 1.5370644331
Epoch: 15 -> Test Accuracy: 45.04
[16, 60] loss: 1.444
[16, 120] loss: 1.434
[16, 180] loss: 1.449
[16, 240] loss: 1.461
[16, 300] loss: 1.434
[16, 360] loss: 1.467
Epoch: 16 -> Loss: 1.54545843601
Epoch: 16 -> Test Accuracy: 45.42
[17, 60] loss: 1.467
[17, 120] loss: 1.426
[17, 180] loss: 1.440
[17, 240] loss: 1.458
[17, 300] loss: 1.439
[17, 360] loss: 1.445
Epoch: 17 -> Loss: 1.40365207195
Epoch: 17 -> Test Accuracy: 45.07
[18, 60] loss: 1.464
[18, 120] loss: 1.444
[18, 180] loss: 1.432
[18, 240] loss: 1.442
[18, 300] loss: 1.439
[18, 360] loss: 1.430
Epoch: 18 -> Loss: 1.54345631599
Epoch: 18 -> Test Accuracy: 45.02
[19, 60] loss: 1.436
[19, 120] loss: 1.438
[19, 180] loss: 1.449
[19, 240] loss: 1.448
[19, 300] loss: 1.452
[19, 360] loss: 1.458
Epoch: 19 -> Loss: 1.54569327831
Epoch: 19 -> Test Accuracy: 44.76
[20, 60] loss: 1.443
[20, 120] loss: 1.439
[20, 180] loss: 1.439
[20, 240] loss: 1.440
[20, 300] loss: 1.434
[20, 360] loss: 1.445
Epoch: 20 -> Loss: 1.4499450922
Epoch: 20 -> Test Accuracy: 44.51
[21, 60] loss: 1.390
[21, 120] loss: 1.365
[21, 180] loss: 1.379
[21, 240] loss: 1.349
[21, 300] loss: 1.343
[21, 360] loss: 1.330
Epoch: 21 -> Loss: 1.20579361916
Epoch: 21 -> Test Accuracy: 48.52
[22, 60] loss: 1.311
[22, 120] loss: 1.325
[22, 180] loss: 1.327
[22, 240] loss: 1.327
[22, 300] loss: 1.317
[22, 360] loss: 1.318
Epoch: 22 -> Loss: 1.28634214401
Epoch: 22 -> Test Accuracy: 49.25
[23, 60] loss: 1.311
[23, 120] loss: 1.300
[23, 180] loss: 1.323
[23, 240] loss: 1.301
[23, 300] loss: 1.323
[23, 360] loss: 1.313
Epoch: 23 -> Loss: 1.2557246685
Epoch: 23 -> Test Accuracy: 48.96
[24, 60] loss: 1.288
[24, 120] loss: 1.296
[24, 180] loss: 1.312
[24, 240] loss: 1.288
[24, 300] loss: 1.303
[24, 360] loss: 1.276
Epoch: 24 -> Loss: 1.45214509964
Epoch: 24 -> Test Accuracy: 49.58
[25, 60] loss: 1.285
[25, 120] loss: 1.310
[25, 180] loss: 1.301
[25, 240] loss: 1.305
[25, 300] loss: 1.289
[25, 360] loss: 1.311
Epoch: 25 -> Loss: 1.18165540695
Epoch: 25 -> Test Accuracy: 49.85
[26, 60] loss: 1.289
[26, 120] loss: 1.295
[26, 180] loss: 1.272
[26, 240] loss: 1.297
[26, 300] loss: 1.296
[26, 360] loss: 1.305
Epoch: 26 -> Loss: 1.342638731
Epoch: 26 -> Test Accuracy: 49.52
[27, 60] loss: 1.299
[27, 120] loss: 1.287
[27, 180] loss: 1.279
[27, 240] loss: 1.294
[27, 300] loss: 1.299
[27, 360] loss: 1.288
Epoch: 27 -> Loss: 1.19322669506
Epoch: 27 -> Test Accuracy: 49.5
[28, 60] loss: 1.267
[28, 120] loss: 1.301
[28, 180] loss: 1.282
[28, 240] loss: 1.282
[28, 300] loss: 1.300
[28, 360] loss: 1.298
Epoch: 28 -> Loss: 1.40955781937
Epoch: 28 -> Test Accuracy: 48.96
[29, 60] loss: 1.263
[29, 120] loss: 1.296
[29, 180] loss: 1.287
[29, 240] loss: 1.306
[29, 300] loss: 1.296
[29, 360] loss: 1.284
Epoch: 29 -> Loss: 1.24729180336
Epoch: 29 -> Test Accuracy: 49.11
[30, 60] loss: 1.292
[30, 120] loss: 1.310
[30, 180] loss: 1.291
[30, 240] loss: 1.283
[30, 300] loss: 1.289
[30, 360] loss: 1.281
Epoch: 30 -> Loss: 1.07997095585
Epoch: 30 -> Test Accuracy: 49.38
[31, 60] loss: 1.292
[31, 120] loss: 1.283
[31, 180] loss: 1.282
[31, 240] loss: 1.289
[31, 300] loss: 1.289
[31, 360] loss: 1.290
Epoch: 31 -> Loss: 1.2045545578
Epoch: 31 -> Test Accuracy: 50.4
[32, 60] loss: 1.290
[32, 120] loss: 1.273
[32, 180] loss: 1.266
[32, 240] loss: 1.285
[32, 300] loss: 1.307
[32, 360] loss: 1.281
Epoch: 32 -> Loss: 1.08216369152
Epoch: 32 -> Test Accuracy: 49.56
[33, 60] loss: 1.251
[33, 120] loss: 1.296
[33, 180] loss: 1.269
[33, 240] loss: 1.302
[33, 300] loss: 1.294
[33, 360] loss: 1.298
Epoch: 33 -> Loss: 1.24276268482
Epoch: 33 -> Test Accuracy: 49.55
[34, 60] loss: 1.265
[34, 120] loss: 1.276
[34, 180] loss: 1.285
[34, 240] loss: 1.277
[34, 300] loss: 1.275
[34, 360] loss: 1.284
Epoch: 34 -> Loss: 1.29300177097
Epoch: 34 -> Test Accuracy: 50.31
[35, 60] loss: 1.271
[35, 120] loss: 1.293
[35, 180] loss: 1.297
[35, 240] loss: 1.288
[35, 300] loss: 1.273
[35, 360] loss: 1.282
Epoch: 35 -> Loss: 1.3820245266
Epoch: 35 -> Test Accuracy: 49.5
[36, 60] loss: 1.278
[36, 120] loss: 1.281
[36, 180] loss: 1.269
[36, 240] loss: 1.286
[36, 300] loss: 1.298
[36, 360] loss: 1.284
Epoch: 36 -> Loss: 1.44006991386
Epoch: 36 -> Test Accuracy: 49.84
[37, 60] loss: 1.275
[37, 120] loss: 1.285
[37, 180] loss: 1.307
[37, 240] loss: 1.279
[37, 300] loss: 1.267
[37, 360] loss: 1.281
Epoch: 37 -> Loss: 1.30595421791
Epoch: 37 -> Test Accuracy: 49.44
[38, 60] loss: 1.291
[38, 120] loss: 1.258
[38, 180] loss: 1.266
[38, 240] loss: 1.292
[38, 300] loss: 1.259
[38, 360] loss: 1.290
Epoch: 38 -> Loss: 1.15272068977
Epoch: 38 -> Test Accuracy: 48.94
[39, 60] loss: 1.287
[39, 120] loss: 1.297
[39, 180] loss: 1.273
[39, 240] loss: 1.272
[39, 300] loss: 1.260
[39, 360] loss: 1.288
Epoch: 39 -> Loss: 1.43694400787
Epoch: 39 -> Test Accuracy: 50.06
[40, 60] loss: 1.276
[40, 120] loss: 1.264
[40, 180] loss: 1.261
[40, 240] loss: 1.286
[40, 300] loss: 1.279
[40, 360] loss: 1.279
Epoch: 40 -> Loss: 1.37329339981
Epoch: 40 -> Test Accuracy: 49.91
[41, 60] loss: 1.243
[41, 120] loss: 1.235
[41, 180] loss: 1.220
[41, 240] loss: 1.211
[41, 300] loss: 1.230
[41, 360] loss: 1.229
Epoch: 41 -> Loss: 1.22143793106
Epoch: 41 -> Test Accuracy: 52.09
[42, 60] loss: 1.219
[42, 120] loss: 1.216
[42, 180] loss: 1.210
[42, 240] loss: 1.188
[42, 300] loss: 1.198
[42, 360] loss: 1.196
Epoch: 42 -> Loss: 1.20635735989
Epoch: 42 -> Test Accuracy: 52.29
[43, 60] loss: 1.213
[43, 120] loss: 1.209
[43, 180] loss: 1.196
[43, 240] loss: 1.181
[43, 300] loss: 1.180
[43, 360] loss: 1.197
Epoch: 43 -> Loss: 1.05017518997
Epoch: 43 -> Test Accuracy: 52.48
[44, 60] loss: 1.185
[44, 120] loss: 1.188
[44, 180] loss: 1.186
[44, 240] loss: 1.203
[44, 300] loss: 1.192
[44, 360] loss: 1.204
Epoch: 44 -> Loss: 1.16620528698
Epoch: 44 -> Test Accuracy: 52.36
[45, 60] loss: 1.197
[45, 120] loss: 1.202
[45, 180] loss: 1.183
[45, 240] loss: 1.192
[45, 300] loss: 1.181
[45, 360] loss: 1.180
Epoch: 45 -> Loss: 1.24895977974
Epoch: 45 -> Test Accuracy: 52.13
[46, 60] loss: 1.174
[46, 120] loss: 1.196
[46, 180] loss: 1.174
[46, 240] loss: 1.178
[46, 300] loss: 1.176
[46, 360] loss: 1.187
Epoch: 46 -> Loss: 1.15635371208
Epoch: 46 -> Test Accuracy: 52.73
[47, 60] loss: 1.169
[47, 120] loss: 1.173
[47, 180] loss: 1.158
[47, 240] loss: 1.159
[47, 300] loss: 1.186
[47, 360] loss: 1.170
Epoch: 47 -> Loss: 1.25530695915
Epoch: 47 -> Test Accuracy: 52.82
[48, 60] loss: 1.161
[48, 120] loss: 1.172
[48, 180] loss: 1.148
[48, 240] loss: 1.169
[48, 300] loss: 1.155
[48, 360] loss: 1.173
Epoch: 48 -> Loss: 1.15647470951
Epoch: 48 -> Test Accuracy: 53.03
[49, 60] loss: 1.180
[49, 120] loss: 1.167
[49, 180] loss: 1.144
[49, 240] loss: 1.184
[49, 300] loss: 1.153
[49, 360] loss: 1.154
Epoch: 49 -> Loss: 1.02462029457
Epoch: 49 -> Test Accuracy: 53.06
[50, 60] loss: 1.152
[50, 120] loss: 1.154
[50, 180] loss: 1.148
[50, 240] loss: 1.178
[50, 300] loss: 1.159
[50, 360] loss: 1.159
Epoch: 50 -> Loss: 1.01887559891
Epoch: 50 -> Test Accuracy: 53.1
[51, 60] loss: 1.158
[51, 120] loss: 1.145
[51, 180] loss: 1.157
[51, 240] loss: 1.177
[51, 300] loss: 1.156
[51, 360] loss: 1.163
Epoch: 51 -> Loss: 1.36545753479
Epoch: 51 -> Test Accuracy: 53.01
[52, 60] loss: 1.170
[52, 120] loss: 1.165
[52, 180] loss: 1.155
[52, 240] loss: 1.168
[52, 300] loss: 1.157
[52, 360] loss: 1.160
Epoch: 52 -> Loss: 1.01275980473
Epoch: 52 -> Test Accuracy: 53.23
[53, 60] loss: 1.151
[53, 120] loss: 1.154
[53, 180] loss: 1.165
[53, 240] loss: 1.142
[53, 300] loss: 1.161
[53, 360] loss: 1.172
Epoch: 53 -> Loss: 1.24386143684
Epoch: 53 -> Test Accuracy: 53.2
[54, 60] loss: 1.126
[54, 120] loss: 1.158
[54, 180] loss: 1.130
[54, 240] loss: 1.156
[54, 300] loss: 1.164
[54, 360] loss: 1.165
Epoch: 54 -> Loss: 1.104996562
Epoch: 54 -> Test Accuracy: 53.26
[55, 60] loss: 1.175
[55, 120] loss: 1.154
[55, 180] loss: 1.152
[55, 240] loss: 1.156
[55, 300] loss: 1.136
[55, 360] loss: 1.138
Epoch: 55 -> Loss: 1.11907505989
Epoch: 55 -> Test Accuracy: 53.21
[56, 60] loss: 1.147
[56, 120] loss: 1.183
[56, 180] loss: 1.155
[56, 240] loss: 1.149
[56, 300] loss: 1.156
[56, 360] loss: 1.154
Epoch: 56 -> Loss: 1.12735331059
Epoch: 56 -> Test Accuracy: 53.32
[57, 60] loss: 1.143
[57, 120] loss: 1.138
[57, 180] loss: 1.153
[57, 240] loss: 1.162
[57, 300] loss: 1.154
[57, 360] loss: 1.143
Epoch: 57 -> Loss: 1.07067346573
Epoch: 57 -> Test Accuracy: 53.12
[58, 60] loss: 1.150
[58, 120] loss: 1.152
[58, 180] loss: 1.143
[58, 240] loss: 1.157
[58, 300] loss: 1.154
[58, 360] loss: 1.156
Epoch: 58 -> Loss: 1.20118761063
Epoch: 58 -> Test Accuracy: 53.73
[59, 60] loss: 1.155
[59, 120] loss: 1.143
[59, 180] loss: 1.137
[59, 240] loss: 1.162
[59, 300] loss: 1.144
[59, 360] loss: 1.154
Epoch: 59 -> Loss: 1.18427968025
Epoch: 59 -> Test Accuracy: 53.41
[60, 60] loss: 1.153
[60, 120] loss: 1.148
[60, 180] loss: 1.152
[60, 240] loss: 1.149
[60, 300] loss: 1.133
[60, 360] loss: 1.184
Epoch: 60 -> Loss: 1.13069331646
Epoch: 60 -> Test Accuracy: 53.65
[61, 60] loss: 1.161
[61, 120] loss: 1.144
[61, 180] loss: 1.146
[61, 240] loss: 1.138
[61, 300] loss: 1.159
[61, 360] loss: 1.142
Epoch: 61 -> Loss: 1.14365255833
Epoch: 61 -> Test Accuracy: 53.59
[62, 60] loss: 1.143
[62, 120] loss: 1.140
[62, 180] loss: 1.155
[62, 240] loss: 1.138
[62, 300] loss: 1.152
[62, 360] loss: 1.145
Epoch: 62 -> Loss: 1.13571286201
Epoch: 62 -> Test Accuracy: 53.6
[63, 60] loss: 1.148
[63, 120] loss: 1.157
[63, 180] loss: 1.155
[63, 240] loss: 1.143
[63, 300] loss: 1.128
[63, 360] loss: 1.150
Epoch: 63 -> Loss: 1.21636080742
Epoch: 63 -> Test Accuracy: 53.39
[64, 60] loss: 1.145
[64, 120] loss: 1.160
[64, 180] loss: 1.127
[64, 240] loss: 1.149
[64, 300] loss: 1.145
[64, 360] loss: 1.168
Epoch: 64 -> Loss: 1.04232823849
Epoch: 64 -> Test Accuracy: 53.85
[65, 60] loss: 1.141
[65, 120] loss: 1.139
[65, 180] loss: 1.148
[65, 240] loss: 1.135
[65, 300] loss: 1.136
[65, 360] loss: 1.153
Epoch: 65 -> Loss: 1.18509340286
Epoch: 65 -> Test Accuracy: 53.51
[66, 60] loss: 1.137
[66, 120] loss: 1.151
[66, 180] loss: 1.158
[66, 240] loss: 1.136
[66, 300] loss: 1.131
[66, 360] loss: 1.143
Epoch: 66 -> Loss: 1.11153292656
Epoch: 66 -> Test Accuracy: 53.58
[67, 60] loss: 1.126
[67, 120] loss: 1.166
[67, 180] loss: 1.139
[67, 240] loss: 1.155
[67, 300] loss: 1.129
[67, 360] loss: 1.154
Epoch: 67 -> Loss: 1.34183907509
Epoch: 67 -> Test Accuracy: 53.62
[68, 60] loss: 1.153
[68, 120] loss: 1.138
[68, 180] loss: 1.126
[68, 240] loss: 1.159
[68, 300] loss: 1.139
[68, 360] loss: 1.133
Epoch: 68 -> Loss: 1.1199092865
Epoch: 68 -> Test Accuracy: 53.69
[69, 60] loss: 1.148
[69, 120] loss: 1.126
[69, 180] loss: 1.155
[69, 240] loss: 1.129
[69, 300] loss: 1.150
[69, 360] loss: 1.146
Epoch: 69 -> Loss: 1.30785059929
Epoch: 69 -> Test Accuracy: 53.72
[70, 60] loss: 1.159
[70, 120] loss: 1.147
[70, 180] loss: 1.123
[70, 240] loss: 1.141
[70, 300] loss: 1.126
[70, 360] loss: 1.146
Epoch: 70 -> Loss: 1.14509820938
Epoch: 70 -> Test Accuracy: 53.64
[71, 60] loss: 1.130
[71, 120] loss: 1.149
[71, 180] loss: 1.148
[71, 240] loss: 1.131
[71, 300] loss: 1.147
[71, 360] loss: 1.149
Epoch: 71 -> Loss: 1.16714203358
Epoch: 71 -> Test Accuracy: 53.67
[72, 60] loss: 1.148
[72, 120] loss: 1.151
[72, 180] loss: 1.148
[72, 240] loss: 1.147
[72, 300] loss: 1.151
[72, 360] loss: 1.129
Epoch: 72 -> Loss: 1.25139403343
Epoch: 72 -> Test Accuracy: 53.52
[73, 60] loss: 1.132
[73, 120] loss: 1.132
[73, 180] loss: 1.145
[73, 240] loss: 1.145
[73, 300] loss: 1.155
[73, 360] loss: 1.129
Epoch: 73 -> Loss: 1.00747525692
Epoch: 73 -> Test Accuracy: 53.7
[74, 60] loss: 1.147
[74, 120] loss: 1.131
[74, 180] loss: 1.149
[74, 240] loss: 1.129
[74, 300] loss: 1.148
[74, 360] loss: 1.133
Epoch: 74 -> Loss: 1.15051519871
Epoch: 74 -> Test Accuracy: 53.53
[75, 60] loss: 1.149
[75, 120] loss: 1.149
[75, 180] loss: 1.135
[75, 240] loss: 1.133
[75, 300] loss: 1.164
[75, 360] loss: 1.124
Epoch: 75 -> Loss: 1.25675094128
Epoch: 75 -> Test Accuracy: 53.54
[76, 60] loss: 1.145
[76, 120] loss: 1.133
[76, 180] loss: 1.144
[76, 240] loss: 1.135
[76, 300] loss: 1.127
[76, 360] loss: 1.135
Epoch: 76 -> Loss: 1.25496768951
Epoch: 76 -> Test Accuracy: 53.44
[77, 60] loss: 1.115
[77, 120] loss: 1.136
[77, 180] loss: 1.138
[77, 240] loss: 1.126
[77, 300] loss: 1.140
[77, 360] loss: 1.151
Epoch: 77 -> Loss: 1.1774790287
Epoch: 77 -> Test Accuracy: 53.59
[78, 60] loss: 1.157
[78, 120] loss: 1.123
[78, 180] loss: 1.119
[78, 240] loss: 1.122
[78, 300] loss: 1.118
[78, 360] loss: 1.147
Epoch: 78 -> Loss: 1.1962954998
Epoch: 78 -> Test Accuracy: 53.46
[79, 60] loss: 1.133
[79, 120] loss: 1.137
[79, 180] loss: 1.149
[79, 240] loss: 1.150
[79, 300] loss: 1.124
[79, 360] loss: 1.130
Epoch: 79 -> Loss: 1.25926578045
Epoch: 79 -> Test Accuracy: 53.64
[80, 60] loss: 1.127
[80, 120] loss: 1.124
[80, 180] loss: 1.157
[80, 240] loss: 1.112
[80, 300] loss: 1.153
[80, 360] loss: 1.130
Epoch: 80 -> Loss: 1.18208158016
Epoch: 80 -> Test Accuracy: 53.63
[81, 60] loss: 1.117
[81, 120] loss: 1.156
[81, 180] loss: 1.124
[81, 240] loss: 1.121
[81, 300] loss: 1.144
[81, 360] loss: 1.134
Epoch: 81 -> Loss: 1.16944134235
Epoch: 81 -> Test Accuracy: 53.72
[82, 60] loss: 1.122
[82, 120] loss: 1.111
[82, 180] loss: 1.142
[82, 240] loss: 1.128
[82, 300] loss: 1.127
[82, 360] loss: 1.156
Epoch: 82 -> Loss: 1.12954199314
Epoch: 82 -> Test Accuracy: 53.77
[83, 60] loss: 1.146
[83, 120] loss: 1.129
[83, 180] loss: 1.141
[83, 240] loss: 1.145
[83, 300] loss: 1.125
[83, 360] loss: 1.123
Epoch: 83 -> Loss: 1.12353086472
Epoch: 83 -> Test Accuracy: 53.68
[84, 60] loss: 1.125
[84, 120] loss: 1.138
[84, 180] loss: 1.117
[84, 240] loss: 1.119
[84, 300] loss: 1.103
[84, 360] loss: 1.155
Epoch: 84 -> Loss: 1.06623804569
Epoch: 84 -> Test Accuracy: 53.4
[85, 60] loss: 1.172
[85, 120] loss: 1.140
[85, 180] loss: 1.127
[85, 240] loss: 1.144
[85, 300] loss: 1.121
[85, 360] loss: 1.126
Epoch: 85 -> Loss: 1.21346509457
Epoch: 85 -> Test Accuracy: 53.92
[86, 60] loss: 1.119
[86, 120] loss: 1.139
[86, 180] loss: 1.129
[86, 240] loss: 1.126
[86, 300] loss: 1.136
[86, 360] loss: 1.134
Epoch: 86 -> Loss: 1.1310646534
Epoch: 86 -> Test Accuracy: 53.52
[87, 60] loss: 1.121
[87, 120] loss: 1.119
[87, 180] loss: 1.146
[87, 240] loss: 1.142
[87, 300] loss: 1.117
[87, 360] loss: 1.149
Epoch: 87 -> Loss: 1.17439734936
Epoch: 87 -> Test Accuracy: 53.7
[88, 60] loss: 1.134
[88, 120] loss: 1.140
[88, 180] loss: 1.125
[88, 240] loss: 1.131
[88, 300] loss: 1.134
[88, 360] loss: 1.128
Epoch: 88 -> Loss: 1.14962530136
Epoch: 88 -> Test Accuracy: 54.18
[89, 60] loss: 1.120
[89, 120] loss: 1.138
[89, 180] loss: 1.125
[89, 240] loss: 1.129
[89, 300] loss: 1.125
[89, 360] loss: 1.122
Epoch: 89 -> Loss: 1.06909906864
Epoch: 89 -> Test Accuracy: 53.87
[90, 60] loss: 1.138
[90, 120] loss: 1.121
[90, 180] loss: 1.117
[90, 240] loss: 1.131
[90, 300] loss: 1.153
[90, 360] loss: 1.127
Epoch: 90 -> Loss: 0.968562722206
Epoch: 90 -> Test Accuracy: 53.92
[91, 60] loss: 1.131
[91, 120] loss: 1.122
[91, 180] loss: 1.118
[91, 240] loss: 1.140
[91, 300] loss: 1.108
[91, 360] loss: 1.129
Epoch: 91 -> Loss: 1.174051404
Epoch: 91 -> Test Accuracy: 53.87
[92, 60] loss: 1.144
[92, 120] loss: 1.129
[92, 180] loss: 1.132
[92, 240] loss: 1.123
[92, 300] loss: 1.128
[92, 360] loss: 1.119
Epoch: 92 -> Loss: 1.17889094353
Epoch: 92 -> Test Accuracy: 53.6
[93, 60] loss: 1.131
[93, 120] loss: 1.130
[93, 180] loss: 1.109
[93, 240] loss: 1.120
[93, 300] loss: 1.127
[93, 360] loss: 1.142
Epoch: 93 -> Loss: 0.920753359795
Epoch: 93 -> Test Accuracy: 53.92
[94, 60] loss: 1.133
[94, 120] loss: 1.138
[94, 180] loss: 1.126
[94, 240] loss: 1.136
[94, 300] loss: 1.139
[94, 360] loss: 1.125
Epoch: 94 -> Loss: 1.15011847019
Epoch: 94 -> Test Accuracy: 53.95
[95, 60] loss: 1.127
[95, 120] loss: 1.141
[95, 180] loss: 1.115
[95, 240] loss: 1.126
[95, 300] loss: 1.118
[95, 360] loss: 1.151
Epoch: 95 -> Loss: 1.27565062046
Epoch: 95 -> Test Accuracy: 54.02
[96, 60] loss: 1.117
[96, 120] loss: 1.108
[96, 180] loss: 1.125
[96, 240] loss: 1.121
[96, 300] loss: 1.119
[96, 360] loss: 1.129
Epoch: 96 -> Loss: 1.32816052437
Epoch: 96 -> Test Accuracy: 54.13
[97, 60] loss: 1.134
[97, 120] loss: 1.155
[97, 180] loss: 1.109
[97, 240] loss: 1.127
[97, 300] loss: 1.130
[97, 360] loss: 1.130
Epoch: 97 -> Loss: 1.05178713799
Epoch: 97 -> Test Accuracy: 54.07
[98, 60] loss: 1.100
[98, 120] loss: 1.136
[98, 180] loss: 1.123
[98, 240] loss: 1.109
[98, 300] loss: 1.129
[98, 360] loss: 1.098
Epoch: 98 -> Loss: 1.00269603729
Epoch: 98 -> Test Accuracy: 54.09
[99, 60] loss: 1.130
[99, 120] loss: 1.112
[99, 180] loss: 1.125
[99, 240] loss: 1.124
[99, 300] loss: 1.121
[99, 360] loss: 1.140
Epoch: 99 -> Loss: 1.08698892593
Epoch: 99 -> Test Accuracy: 54.22
[100, 60] loss: 1.101
[100, 120] loss: 1.119
[100, 180] loss: 1.129
[100, 240] loss: 1.129
[100, 300] loss: 1.136
[100, 360] loss: 1.118
Epoch: 100 -> Loss: 1.34540462494
Epoch: 100 -> Test Accuracy: 54.07
Finished Training
In [15]:
# train ConvClassifiers on feature map of net_3block
conv_block3_loss_log, _, conv_block3_test_accuracy_log, _, _ = tr.train_all_blocks(3, 10, [0.1, 0.02, 0.004, 0.0008], 
    [35, 70, 85, 100], 0.9, 5e-4, net_block3, criterion, trainloader, None, testloader, use_ConvClassifier=True) 
[1, 60] loss: 1.371
[1, 120] loss: 1.023
[1, 180] loss: 0.913
[1, 240] loss: 0.887
[1, 300] loss: 0.824
[1, 360] loss: 0.783
Epoch: 1 -> Loss: 0.617035627365
Epoch: 1 -> Test Accuracy: 70.87
[2, 60] loss: 0.747
[2, 120] loss: 0.703
[2, 180] loss: 0.689
[2, 240] loss: 0.676
[2, 300] loss: 0.673
[2, 360] loss: 0.660
Epoch: 2 -> Loss: 0.749189436436
Epoch: 2 -> Test Accuracy: 74.43
[3, 60] loss: 0.627
[3, 120] loss: 0.612
[3, 180] loss: 0.619
[3, 240] loss: 0.611
[3, 300] loss: 0.628
[3, 360] loss: 0.587
Epoch: 3 -> Loss: 0.666461706161
Epoch: 3 -> Test Accuracy: 77.75
[4, 60] loss: 0.566
[4, 120] loss: 0.570
[4, 180] loss: 0.566
[4, 240] loss: 0.556
[4, 300] loss: 0.553
[4, 360] loss: 0.576
Epoch: 4 -> Loss: 0.634184956551
Epoch: 4 -> Test Accuracy: 77.39
[5, 60] loss: 0.521
[5, 120] loss: 0.541
[5, 180] loss: 0.559
[5, 240] loss: 0.535
[5, 300] loss: 0.520
[5, 360] loss: 0.528
Epoch: 5 -> Loss: 0.685723721981
Epoch: 5 -> Test Accuracy: 78.43
[6, 60] loss: 0.509
[6, 120] loss: 0.506
[6, 180] loss: 0.529
[6, 240] loss: 0.504
[6, 300] loss: 0.516
[6, 360] loss: 0.521
Epoch: 6 -> Loss: 0.714321732521
Epoch: 6 -> Test Accuracy: 77.98
[7, 60] loss: 0.514
[7, 120] loss: 0.508
[7, 180] loss: 0.497
[7, 240] loss: 0.484
[7, 300] loss: 0.491
[7, 360] loss: 0.506
Epoch: 7 -> Loss: 0.479330956936
Epoch: 7 -> Test Accuracy: 79.51
[8, 60] loss: 0.460
[8, 120] loss: 0.470
[8, 180] loss: 0.499
[8, 240] loss: 0.493
[8, 300] loss: 0.505
[8, 360] loss: 0.483
Epoch: 8 -> Loss: 0.708630979061
Epoch: 8 -> Test Accuracy: 79.96
[9, 60] loss: 0.445
[9, 120] loss: 0.464
[9, 180] loss: 0.486
[9, 240] loss: 0.468
[9, 300] loss: 0.493
[9, 360] loss: 0.472
Epoch: 9 -> Loss: 0.668149590492
Epoch: 9 -> Test Accuracy: 80.27
[10, 60] loss: 0.449
[10, 120] loss: 0.466
[10, 180] loss: 0.485
[10, 240] loss: 0.492
[10, 300] loss: 0.481
[10, 360] loss: 0.469
Epoch: 10 -> Loss: 0.470273196697
Epoch: 10 -> Test Accuracy: 80.33
[11, 60] loss: 0.439
[11, 120] loss: 0.468
[11, 180] loss: 0.455
[11, 240] loss: 0.461
[11, 300] loss: 0.471
[11, 360] loss: 0.454
Epoch: 11 -> Loss: 0.494262129068
Epoch: 11 -> Test Accuracy: 80.4
[12, 60] loss: 0.447
[12, 120] loss: 0.437
[12, 180] loss: 0.464
[12, 240] loss: 0.447
[12, 300] loss: 0.460
[12, 360] loss: 0.468
Epoch: 12 -> Loss: 0.424614042044
Epoch: 12 -> Test Accuracy: 80.15
[13, 60] loss: 0.431
[13, 120] loss: 0.437
[13, 180] loss: 0.458
[13, 240] loss: 0.448
[13, 300] loss: 0.456
[13, 360] loss: 0.459
Epoch: 13 -> Loss: 0.420501470566
Epoch: 13 -> Test Accuracy: 81.55
[14, 60] loss: 0.425
[14, 120] loss: 0.447
[14, 180] loss: 0.428
[14, 240] loss: 0.441
[14, 300] loss: 0.462
[14, 360] loss: 0.445
Epoch: 14 -> Loss: 0.493283182383
Epoch: 14 -> Test Accuracy: 80.95
[15, 60] loss: 0.415
[15, 120] loss: 0.409
[15, 180] loss: 0.452
[15, 240] loss: 0.435
[15, 300] loss: 0.458
[15, 360] loss: 0.453
Epoch: 15 -> Loss: 0.687053024769
Epoch: 15 -> Test Accuracy: 81.13
[16, 60] loss: 0.426
[16, 120] loss: 0.402
[16, 180] loss: 0.449
[16, 240] loss: 0.436
[16, 300] loss: 0.451
[16, 360] loss: 0.433
Epoch: 16 -> Loss: 0.525857210159
Epoch: 16 -> Test Accuracy: 82.14
[17, 60] loss: 0.412
[17, 120] loss: 0.425
[17, 180] loss: 0.454
[17, 240] loss: 0.423
[17, 300] loss: 0.433
[17, 360] loss: 0.444
Epoch: 17 -> Loss: 0.48986697197
Epoch: 17 -> Test Accuracy: 81.2
[18, 60] loss: 0.416
[18, 120] loss: 0.417
[18, 180] loss: 0.447
[18, 240] loss: 0.436
[18, 300] loss: 0.441
[18, 360] loss: 0.430
Epoch: 18 -> Loss: 0.359789043665
Epoch: 18 -> Test Accuracy: 81.58
[19, 60] loss: 0.400
[19, 120] loss: 0.415
[19, 180] loss: 0.427
[19, 240] loss: 0.431
[19, 300] loss: 0.448
[19, 360] loss: 0.434
Epoch: 19 -> Loss: 0.476075470448
Epoch: 19 -> Test Accuracy: 81.62
[20, 60] loss: 0.408
[20, 120] loss: 0.411
[20, 180] loss: 0.412
[20, 240] loss: 0.436
[20, 300] loss: 0.436
[20, 360] loss: 0.406
Epoch: 20 -> Loss: 0.665274560452
Epoch: 20 -> Test Accuracy: 80.63
[21, 60] loss: 0.413
[21, 120] loss: 0.413
[21, 180] loss: 0.412
[21, 240] loss: 0.431
[21, 300] loss: 0.426
[21, 360] loss: 0.450
Epoch: 21 -> Loss: 0.453517138958
Epoch: 21 -> Test Accuracy: 82.28
[22, 60] loss: 0.392
[22, 120] loss: 0.415
[22, 180] loss: 0.409
[22, 240] loss: 0.427
[22, 300] loss: 0.420
[22, 360] loss: 0.428
Epoch: 22 -> Loss: 0.365581929684
Epoch: 22 -> Test Accuracy: 79.74
[23, 60] loss: 0.400
[23, 120] loss: 0.415
[23, 180] loss: 0.434
[23, 240] loss: 0.427
[23, 300] loss: 0.427
[23, 360] loss: 0.436
Epoch: 23 -> Loss: 0.307698786259
Epoch: 23 -> Test Accuracy: 80.45
[24, 60] loss: 0.403
[24, 120] loss: 0.411
[24, 180] loss: 0.403
[24, 240] loss: 0.418
[24, 300] loss: 0.409
[24, 360] loss: 0.424
Epoch: 24 -> Loss: 0.541094362736
Epoch: 24 -> Test Accuracy: 82.16
[25, 60] loss: 0.412
[25, 120] loss: 0.401
[25, 180] loss: 0.421
[25, 240] loss: 0.435
[25, 300] loss: 0.417
[25, 360] loss: 0.424
Epoch: 25 -> Loss: 0.558412909508
Epoch: 25 -> Test Accuracy: 81.44
[26, 60] loss: 0.409
[26, 120] loss: 0.408
[26, 180] loss: 0.403
[26, 240] loss: 0.423
[26, 300] loss: 0.421
[26, 360] loss: 0.410
Epoch: 26 -> Loss: 0.406867265701
Epoch: 26 -> Test Accuracy: 81.33
[27, 60] loss: 0.391
[27, 120] loss: 0.404
[27, 180] loss: 0.427
[27, 240] loss: 0.406
[27, 300] loss: 0.413
[27, 360] loss: 0.427
Epoch: 27 -> Loss: 0.352363586426
Epoch: 27 -> Test Accuracy: 81.67
[28, 60] loss: 0.389
[28, 120] loss: 0.404
[28, 180] loss: 0.415
[28, 240] loss: 0.413
[28, 300] loss: 0.414
[28, 360] loss: 0.421
Epoch: 28 -> Loss: 0.485638141632
Epoch: 28 -> Test Accuracy: 81.59
[29, 60] loss: 0.389
[29, 120] loss: 0.400
[29, 180] loss: 0.399
[29, 240] loss: 0.424
[29, 300] loss: 0.408
[29, 360] loss: 0.434
Epoch: 29 -> Loss: 0.435752779245
Epoch: 29 -> Test Accuracy: 82.08
[30, 60] loss: 0.392
[30, 120] loss: 0.401
[30, 180] loss: 0.392
[30, 240] loss: 0.415
[30, 300] loss: 0.417
[30, 360] loss: 0.420
Epoch: 30 -> Loss: 0.471777528524
Epoch: 30 -> Test Accuracy: 81.43
[31, 60] loss: 0.414
[31, 120] loss: 0.396
[31, 180] loss: 0.409
[31, 240] loss: 0.416
[31, 300] loss: 0.404
[31, 360] loss: 0.416
Epoch: 31 -> Loss: 0.586894094944
Epoch: 31 -> Test Accuracy: 81.65
[32, 60] loss: 0.394
[32, 120] loss: 0.389
[32, 180] loss: 0.424
[32, 240] loss: 0.409
[32, 300] loss: 0.413
[32, 360] loss: 0.408
Epoch: 32 -> Loss: 0.67364937067
Epoch: 32 -> Test Accuracy: 81.55
[33, 60] loss: 0.387
[33, 120] loss: 0.389
[33, 180] loss: 0.415
[33, 240] loss: 0.418
[33, 300] loss: 0.405
[33, 360] loss: 0.398
Epoch: 33 -> Loss: 0.429774224758
Epoch: 33 -> Test Accuracy: 81.84
[34, 60] loss: 0.385
[34, 120] loss: 0.392
[34, 180] loss: 0.420
[34, 240] loss: 0.404
[34, 300] loss: 0.402
[34, 360] loss: 0.434
Epoch: 34 -> Loss: 0.351295530796
Epoch: 34 -> Test Accuracy: 82.5
[35, 60] loss: 0.369
[35, 120] loss: 0.407
[35, 180] loss: 0.403
[35, 240] loss: 0.411
[35, 300] loss: 0.416
[35, 360] loss: 0.411
Epoch: 35 -> Loss: 0.46245008707
Epoch: 35 -> Test Accuracy: 81.89
[36, 60] loss: 0.307
[36, 120] loss: 0.282
[36, 180] loss: 0.271
[36, 240] loss: 0.284
[36, 300] loss: 0.279
[36, 360] loss: 0.271
Epoch: 36 -> Loss: 0.511943459511
Epoch: 36 -> Test Accuracy: 85.62
[37, 60] loss: 0.255
[37, 120] loss: 0.250
[37, 180] loss: 0.244
[37, 240] loss: 0.248
[37, 300] loss: 0.248
[37, 360] loss: 0.251
Epoch: 37 -> Loss: 0.307639151812
Epoch: 37 -> Test Accuracy: 86.44
[38, 60] loss: 0.231
[38, 120] loss: 0.237
[38, 180] loss: 0.247
[38, 240] loss: 0.225
[38, 300] loss: 0.230
[38, 360] loss: 0.252
Epoch: 38 -> Loss: 0.353818029165
Epoch: 38 -> Test Accuracy: 85.76
[39, 60] loss: 0.223
[39, 120] loss: 0.223
[39, 180] loss: 0.219
[39, 240] loss: 0.231
[39, 300] loss: 0.237
[39, 360] loss: 0.242
Epoch: 39 -> Loss: 0.209128811955
Epoch: 39 -> Test Accuracy: 85.6
[40, 60] loss: 0.212
[40, 120] loss: 0.229
[40, 180] loss: 0.229
[40, 240] loss: 0.229
[40, 300] loss: 0.215
[40, 360] loss: 0.229
Epoch: 40 -> Loss: 0.214634135365
Epoch: 40 -> Test Accuracy: 86.36
[41, 60] loss: 0.203
[41, 120] loss: 0.227
[41, 180] loss: 0.219
[41, 240] loss: 0.222
[41, 300] loss: 0.232
[41, 360] loss: 0.238
Epoch: 41 -> Loss: 0.193170338869
Epoch: 41 -> Test Accuracy: 85.79
[42, 60] loss: 0.210
[42, 120] loss: 0.210
[42, 180] loss: 0.213
[42, 240] loss: 0.220
[42, 300] loss: 0.226
[42, 360] loss: 0.229
Epoch: 42 -> Loss: 0.367429107428
Epoch: 42 -> Test Accuracy: 85.27
[43, 60] loss: 0.208
[43, 120] loss: 0.212
[43, 180] loss: 0.223
[43, 240] loss: 0.226
[43, 300] loss: 0.225
[43, 360] loss: 0.230
Epoch: 43 -> Loss: 0.246880248189
Epoch: 43 -> Test Accuracy: 86.26
[44, 60] loss: 0.211
[44, 120] loss: 0.199
[44, 180] loss: 0.218
[44, 240] loss: 0.218
[44, 300] loss: 0.224
[44, 360] loss: 0.239
Epoch: 44 -> Loss: 0.308743089437
Epoch: 44 -> Test Accuracy: 85.1
[45, 60] loss: 0.207
[45, 120] loss: 0.211
[45, 180] loss: 0.216
[45, 240] loss: 0.227
[45, 300] loss: 0.221
[45, 360] loss: 0.218
Epoch: 45 -> Loss: 0.193989947438
Epoch: 45 -> Test Accuracy: 84.7
[46, 60] loss: 0.211
[46, 120] loss: 0.204
[46, 180] loss: 0.213
[46, 240] loss: 0.218
[46, 300] loss: 0.221
[46, 360] loss: 0.237
Epoch: 46 -> Loss: 0.173785120249
Epoch: 46 -> Test Accuracy: 85.62
[47, 60] loss: 0.209
[47, 120] loss: 0.213
[47, 180] loss: 0.213
[47, 240] loss: 0.232
[47, 300] loss: 0.227
[47, 360] loss: 0.235
Epoch: 47 -> Loss: 0.215796589851
Epoch: 47 -> Test Accuracy: 85.7
[48, 60] loss: 0.197
[48, 120] loss: 0.203
[48, 180] loss: 0.215
[48, 240] loss: 0.224
[48, 300] loss: 0.228
[48, 360] loss: 0.239
Epoch: 48 -> Loss: 0.272399038076
Epoch: 48 -> Test Accuracy: 84.95
[49, 60] loss: 0.205
[49, 120] loss: 0.206
[49, 180] loss: 0.222
[49, 240] loss: 0.219
[49, 300] loss: 0.231
[49, 360] loss: 0.235
Epoch: 49 -> Loss: 0.123649761081
Epoch: 49 -> Test Accuracy: 84.89
[50, 60] loss: 0.205
[50, 120] loss: 0.210
[50, 180] loss: 0.224
[50, 240] loss: 0.219
[50, 300] loss: 0.220
[50, 360] loss: 0.233
Epoch: 50 -> Loss: 0.227295681834
Epoch: 50 -> Test Accuracy: 85.24
[51, 60] loss: 0.205
[51, 120] loss: 0.211
[51, 180] loss: 0.209
[51, 240] loss: 0.217
[51, 300] loss: 0.221
[51, 360] loss: 0.226
Epoch: 51 -> Loss: 0.302188068628
Epoch: 51 -> Test Accuracy: 85.78
[52, 60] loss: 0.203
[52, 120] loss: 0.195
[52, 180] loss: 0.214
[52, 240] loss: 0.216
[52, 300] loss: 0.229
[52, 360] loss: 0.228
Epoch: 52 -> Loss: 0.26025018096
Epoch: 52 -> Test Accuracy: 85.12
[53, 60] loss: 0.198
[53, 120] loss: 0.201
[53, 180] loss: 0.228
[53, 240] loss: 0.210
[53, 300] loss: 0.228
[53, 360] loss: 0.236
Epoch: 53 -> Loss: 0.345288306475
Epoch: 53 -> Test Accuracy: 85.29
[54, 60] loss: 0.199
[54, 120] loss: 0.199
[54, 180] loss: 0.202
[54, 240] loss: 0.231
[54, 300] loss: 0.223
[54, 360] loss: 0.232
Epoch: 54 -> Loss: 0.20453453064
Epoch: 54 -> Test Accuracy: 85.02
[55, 60] loss: 0.212
[55, 120] loss: 0.215
[55, 180] loss: 0.216
[55, 240] loss: 0.211
[55, 300] loss: 0.222
[55, 360] loss: 0.232
Epoch: 55 -> Loss: 0.144998937845
Epoch: 55 -> Test Accuracy: 84.86
[56, 60] loss: 0.202
[56, 120] loss: 0.198
[56, 180] loss: 0.212
[56, 240] loss: 0.219
[56, 300] loss: 0.236
[56, 360] loss: 0.223
Epoch: 56 -> Loss: 0.226112693548
Epoch: 56 -> Test Accuracy: 84.92
[57, 60] loss: 0.195
[57, 120] loss: 0.208
[57, 180] loss: 0.218
[57, 240] loss: 0.213
[57, 300] loss: 0.221
[57, 360] loss: 0.228
Epoch: 57 -> Loss: 0.235637187958
Epoch: 57 -> Test Accuracy: 84.86
[58, 60] loss: 0.198
[58, 120] loss: 0.211
[58, 180] loss: 0.207
[58, 240] loss: 0.219
[58, 300] loss: 0.217
[58, 360] loss: 0.224
Epoch: 58 -> Loss: 0.234650462866
Epoch: 58 -> Test Accuracy: 84.77
[59, 60] loss: 0.199
[59, 120] loss: 0.198
[59, 180] loss: 0.211
[59, 240] loss: 0.218
[59, 300] loss: 0.219
[59, 360] loss: 0.233
Epoch: 59 -> Loss: 0.1255761832
Epoch: 59 -> Test Accuracy: 84.43
[60, 60] loss: 0.197
[60, 120] loss: 0.209
[60, 180] loss: 0.204
[60, 240] loss: 0.217
[60, 300] loss: 0.216
[60, 360] loss: 0.220
Epoch: 60 -> Loss: 0.277145057917
Epoch: 60 -> Test Accuracy: 85.29
[61, 60] loss: 0.188
[61, 120] loss: 0.190
[61, 180] loss: 0.207
[61, 240] loss: 0.213
[61, 300] loss: 0.216
[61, 360] loss: 0.226
Epoch: 61 -> Loss: 0.139841303229
Epoch: 61 -> Test Accuracy: 85.05
[62, 60] loss: 0.196
[62, 120] loss: 0.213
[62, 180] loss: 0.198
[62, 240] loss: 0.217
[62, 300] loss: 0.217
[62, 360] loss: 0.228
Epoch: 62 -> Loss: 0.245643734932
Epoch: 62 -> Test Accuracy: 85.33
[63, 60] loss: 0.193
[63, 120] loss: 0.202
[63, 180] loss: 0.208
[63, 240] loss: 0.200
[63, 300] loss: 0.211
[63, 360] loss: 0.232
Epoch: 63 -> Loss: 0.336574912071
Epoch: 63 -> Test Accuracy: 85.29
[64, 60] loss: 0.205
[64, 120] loss: 0.202
[64, 180] loss: 0.214
[64, 240] loss: 0.210
[64, 300] loss: 0.210
[64, 360] loss: 0.222
Epoch: 64 -> Loss: 0.157260462642
Epoch: 64 -> Test Accuracy: 85.26
[65, 60] loss: 0.196
[65, 120] loss: 0.200
[65, 180] loss: 0.195
[65, 240] loss: 0.204
[65, 300] loss: 0.212
[65, 360] loss: 0.219
Epoch: 65 -> Loss: 0.289101332426
Epoch: 65 -> Test Accuracy: 84.37
[66, 60] loss: 0.190
[66, 120] loss: 0.204
[66, 180] loss: 0.220
[66, 240] loss: 0.210
[66, 300] loss: 0.215
[66, 360] loss: 0.218
Epoch: 66 -> Loss: 0.192337989807
Epoch: 66 -> Test Accuracy: 84.95
[67, 60] loss: 0.195
[67, 120] loss: 0.191
[67, 180] loss: 0.213
[67, 240] loss: 0.207
[67, 300] loss: 0.223
[67, 360] loss: 0.218
Epoch: 67 -> Loss: 0.191490486264
Epoch: 67 -> Test Accuracy: 84.88
[68, 60] loss: 0.191
[68, 120] loss: 0.189
[68, 180] loss: 0.210
[68, 240] loss: 0.216
[68, 300] loss: 0.226
[68, 360] loss: 0.222
Epoch: 68 -> Loss: 0.217858999968
Epoch: 68 -> Test Accuracy: 83.97
[69, 60] loss: 0.194
[69, 120] loss: 0.185
[69, 180] loss: 0.195
[69, 240] loss: 0.211
[69, 300] loss: 0.209
[69, 360] loss: 0.219
Epoch: 69 -> Loss: 0.202600002289
Epoch: 69 -> Test Accuracy: 84.85
[70, 60] loss: 0.196
[70, 120] loss: 0.191
[70, 180] loss: 0.199
[70, 240] loss: 0.212
[70, 300] loss: 0.213
[70, 360] loss: 0.214
Epoch: 70 -> Loss: 0.217269584537
Epoch: 70 -> Test Accuracy: 85.36
[71, 60] loss: 0.173
[71, 120] loss: 0.144
[71, 180] loss: 0.140
[71, 240] loss: 0.136
[71, 300] loss: 0.140
[71, 360] loss: 0.133
Epoch: 71 -> Loss: 0.0861133784056
Epoch: 71 -> Test Accuracy: 86.88
[72, 60] loss: 0.121
[72, 120] loss: 0.121
[72, 180] loss: 0.122
[72, 240] loss: 0.120
[72, 300] loss: 0.128
[72, 360] loss: 0.124
Epoch: 72 -> Loss: 0.152239322662
Epoch: 72 -> Test Accuracy: 86.78
[73, 60] loss: 0.124
[73, 120] loss: 0.119
[73, 180] loss: 0.115
[73, 240] loss: 0.116
[73, 300] loss: 0.124
[73, 360] loss: 0.125
Epoch: 73 -> Loss: 0.24748647213
Epoch: 73 -> Test Accuracy: 86.97
[74, 60] loss: 0.112
[74, 120] loss: 0.109
[74, 180] loss: 0.112
[74, 240] loss: 0.122
[74, 300] loss: 0.112
[74, 360] loss: 0.122
Epoch: 74 -> Loss: 0.152580738068
Epoch: 74 -> Test Accuracy: 86.8
[75, 60] loss: 0.109
[75, 120] loss: 0.115
[75, 180] loss: 0.109
[75, 240] loss: 0.119
[75, 300] loss: 0.110
[75, 360] loss: 0.109
Epoch: 75 -> Loss: 0.0667220279574
Epoch: 75 -> Test Accuracy: 86.88
[76, 60] loss: 0.103
[76, 120] loss: 0.104
[76, 180] loss: 0.103
[76, 240] loss: 0.110
[76, 300] loss: 0.111
[76, 360] loss: 0.119
Epoch: 76 -> Loss: 0.104675829411
Epoch: 76 -> Test Accuracy: 87.01
[77, 60] loss: 0.105
[77, 120] loss: 0.109
[77, 180] loss: 0.105
[77, 240] loss: 0.107
[77, 300] loss: 0.109
[77, 360] loss: 0.112
Epoch: 77 -> Loss: 0.0587496869266
Epoch: 77 -> Test Accuracy: 86.84
[78, 60] loss: 0.104
[78, 120] loss: 0.103
[78, 180] loss: 0.107
[78, 240] loss: 0.102
[78, 300] loss: 0.100
[78, 360] loss: 0.104
Epoch: 78 -> Loss: 0.119968272746
Epoch: 78 -> Test Accuracy: 87.15
[79, 60] loss: 0.098
[79, 120] loss: 0.103
[79, 180] loss: 0.099
[79, 240] loss: 0.100
[79, 300] loss: 0.107
[79, 360] loss: 0.110
Epoch: 79 -> Loss: 0.148834779859
Epoch: 79 -> Test Accuracy: 86.81
[80, 60] loss: 0.101
[80, 120] loss: 0.109
[80, 180] loss: 0.102
[80, 240] loss: 0.096
[80, 300] loss: 0.102
[80, 360] loss: 0.108
Epoch: 80 -> Loss: 0.112207576632
Epoch: 80 -> Test Accuracy: 86.9
[81, 60] loss: 0.092
[81, 120] loss: 0.103
[81, 180] loss: 0.103
[81, 240] loss: 0.101
[81, 300] loss: 0.101
[81, 360] loss: 0.100
Epoch: 81 -> Loss: 0.0783820748329
Epoch: 81 -> Test Accuracy: 86.72
[82, 60] loss: 0.097
[82, 120] loss: 0.098
[82, 180] loss: 0.098
[82, 240] loss: 0.094
[82, 300] loss: 0.096
[82, 360] loss: 0.096
Epoch: 82 -> Loss: 0.147284641862
Epoch: 82 -> Test Accuracy: 86.91
[83, 60] loss: 0.097
[83, 120] loss: 0.100
[83, 180] loss: 0.095
[83, 240] loss: 0.105
[83, 300] loss: 0.100
[83, 360] loss: 0.096
Epoch: 83 -> Loss: 0.0895229056478
Epoch: 83 -> Test Accuracy: 86.76
[84, 60] loss: 0.093
[84, 120] loss: 0.098
[84, 180] loss: 0.095
[84, 240] loss: 0.098
[84, 300] loss: 0.099
[84, 360] loss: 0.100
Epoch: 84 -> Loss: 0.173047661781
Epoch: 84 -> Test Accuracy: 86.94
[85, 60] loss: 0.092
[85, 120] loss: 0.090
[85, 180] loss: 0.095
[85, 240] loss: 0.098
[85, 300] loss: 0.094
[85, 360] loss: 0.098
Epoch: 85 -> Loss: 0.0852631404996
Epoch: 85 -> Test Accuracy: 87.11
[86, 60] loss: 0.092
[86, 120] loss: 0.083
[86, 180] loss: 0.082
[86, 240] loss: 0.079
[86, 300] loss: 0.082
[86, 360] loss: 0.087
Epoch: 86 -> Loss: 0.117199338973
Epoch: 86 -> Test Accuracy: 87.23
[87, 60] loss: 0.080
[87, 120] loss: 0.077
[87, 180] loss: 0.080
[87, 240] loss: 0.082
[87, 300] loss: 0.080
[87, 360] loss: 0.081
Epoch: 87 -> Loss: 0.0730190724134
Epoch: 87 -> Test Accuracy: 87.05
[88, 60] loss: 0.080
[88, 120] loss: 0.079
[88, 180] loss: 0.082
[88, 240] loss: 0.077
[88, 300] loss: 0.079
[88, 360] loss: 0.077
Epoch: 88 -> Loss: 0.0719773620367
Epoch: 88 -> Test Accuracy: 87.09
[89, 60] loss: 0.079
[89, 120] loss: 0.081
[89, 180] loss: 0.074
[89, 240] loss: 0.079
[89, 300] loss: 0.074
[89, 360] loss: 0.078
Epoch: 89 -> Loss: 0.0586576089263
Epoch: 89 -> Test Accuracy: 87.1
[90, 60] loss: 0.076
[90, 120] loss: 0.082
[90, 180] loss: 0.081
[90, 240] loss: 0.081
[90, 300] loss: 0.075
[90, 360] loss: 0.080
Epoch: 90 -> Loss: 0.068951241672
Epoch: 90 -> Test Accuracy: 87.01
[91, 60] loss: 0.077
[91, 120] loss: 0.075
[91, 180] loss: 0.078
[91, 240] loss: 0.079
[91, 300] loss: 0.082
[91, 360] loss: 0.078
Epoch: 91 -> Loss: 0.062657892704
Epoch: 91 -> Test Accuracy: 87.12
[92, 60] loss: 0.079
[92, 120] loss: 0.078
[92, 180] loss: 0.076
[92, 240] loss: 0.078
[92, 300] loss: 0.080
[92, 360] loss: 0.080
Epoch: 92 -> Loss: 0.0748406276107
Epoch: 92 -> Test Accuracy: 86.95
[93, 60] loss: 0.071
[93, 120] loss: 0.076
[93, 180] loss: 0.076
[93, 240] loss: 0.073
[93, 300] loss: 0.082
[93, 360] loss: 0.079
Epoch: 93 -> Loss: 0.0712976232171
Epoch: 93 -> Test Accuracy: 87.08
[94, 60] loss: 0.073
[94, 120] loss: 0.078
[94, 180] loss: 0.079
[94, 240] loss: 0.077
[94, 300] loss: 0.078
[94, 360] loss: 0.077
Epoch: 94 -> Loss: 0.136106818914
Epoch: 94 -> Test Accuracy: 87.23
[95, 60] loss: 0.077
[95, 120] loss: 0.079
[95, 180] loss: 0.076
[95, 240] loss: 0.080
[95, 300] loss: 0.079
[95, 360] loss: 0.075
Epoch: 95 -> Loss: 0.0827887803316
Epoch: 95 -> Test Accuracy: 86.9
[96, 60] loss: 0.075
[96, 120] loss: 0.078
[96, 180] loss: 0.075
[96, 240] loss: 0.078
[96, 300] loss: 0.077
[96, 360] loss: 0.075
Epoch: 96 -> Loss: 0.074126958847
Epoch: 96 -> Test Accuracy: 87.09
[97, 60] loss: 0.077
[97, 120] loss: 0.073
[97, 180] loss: 0.077
[97, 240] loss: 0.075
[97, 300] loss: 0.077
[97, 360] loss: 0.078
Epoch: 97 -> Loss: 0.123150423169
Epoch: 97 -> Test Accuracy: 86.99
[98, 60] loss: 0.076
[98, 120] loss: 0.077
[98, 180] loss: 0.076
[98, 240] loss: 0.071
[98, 300] loss: 0.074
[98, 360] loss: 0.079
Epoch: 98 -> Loss: 0.0828704237938
Epoch: 98 -> Test Accuracy: 87.05
[99, 60] loss: 0.074
[99, 120] loss: 0.075
[99, 180] loss: 0.071
[99, 240] loss: 0.077
[99, 300] loss: 0.078
[99, 360] loss: 0.077
Epoch: 99 -> Loss: 0.0612016692758
Epoch: 99 -> Test Accuracy: 87.13
[100, 60] loss: 0.069
[100, 120] loss: 0.075
[100, 180] loss: 0.073
[100, 240] loss: 0.074
[100, 300] loss: 0.074
[100, 360] loss: 0.075
Epoch: 100 -> Loss: 0.038023866713
Epoch: 100 -> Test Accuracy: 86.85
Finished Training
[1, 60] loss: 0.899
[1, 120] loss: 0.623
[1, 180] loss: 0.573
[1, 240] loss: 0.570
[1, 300] loss: 0.516
[1, 360] loss: 0.494
Epoch: 1 -> Loss: 0.527269244194
Epoch: 1 -> Test Accuracy: 81.08
[2, 60] loss: 0.455
[2, 120] loss: 0.454
[2, 180] loss: 0.448
[2, 240] loss: 0.427
[2, 300] loss: 0.435
[2, 360] loss: 0.446
Epoch: 2 -> Loss: 0.406312793493
Epoch: 2 -> Test Accuracy: 83.37
[3, 60] loss: 0.394
[3, 120] loss: 0.399
[3, 180] loss: 0.400
[3, 240] loss: 0.399
[3, 300] loss: 0.408
[3, 360] loss: 0.392
Epoch: 3 -> Loss: 0.339932471514
Epoch: 3 -> Test Accuracy: 83.35
[4, 60] loss: 0.363
[4, 120] loss: 0.368
[4, 180] loss: 0.360
[4, 240] loss: 0.381
[4, 300] loss: 0.382
[4, 360] loss: 0.384
Epoch: 4 -> Loss: 0.269151031971
Epoch: 4 -> Test Accuracy: 84.44
[5, 60] loss: 0.338
[5, 120] loss: 0.354
[5, 180] loss: 0.350
[5, 240] loss: 0.341
[5, 300] loss: 0.366
[5, 360] loss: 0.356
Epoch: 5 -> Loss: 0.325666487217
Epoch: 5 -> Test Accuracy: 84.63
[6, 60] loss: 0.314
[6, 120] loss: 0.327
[6, 180] loss: 0.335
[6, 240] loss: 0.342
[6, 300] loss: 0.347
[6, 360] loss: 0.356
Epoch: 6 -> Loss: 0.313348770142
Epoch: 6 -> Test Accuracy: 83.96
[7, 60] loss: 0.319
[7, 120] loss: 0.331
[7, 180] loss: 0.324
[7, 240] loss: 0.321
[7, 300] loss: 0.341
[7, 360] loss: 0.336
Epoch: 7 -> Loss: 0.443416684866
Epoch: 7 -> Test Accuracy: 83.39
[8, 60] loss: 0.300
[8, 120] loss: 0.310
[8, 180] loss: 0.317
[8, 240] loss: 0.321
[8, 300] loss: 0.342
[8, 360] loss: 0.336
Epoch: 8 -> Loss: 0.214324861765
Epoch: 8 -> Test Accuracy: 85.53
[9, 60] loss: 0.305
[9, 120] loss: 0.302
[9, 180] loss: 0.305
[9, 240] loss: 0.325
[9, 300] loss: 0.327
[9, 360] loss: 0.315
Epoch: 9 -> Loss: 0.455975621939
Epoch: 9 -> Test Accuracy: 85.41
[10, 60] loss: 0.276
[10, 120] loss: 0.294
[10, 180] loss: 0.309
[10, 240] loss: 0.295
[10, 300] loss: 0.325
[10, 360] loss: 0.320
Epoch: 10 -> Loss: 0.491630464792
Epoch: 10 -> Test Accuracy: 85.57
[11, 60] loss: 0.271
[11, 120] loss: 0.285
[11, 180] loss: 0.289
[11, 240] loss: 0.304
[11, 300] loss: 0.339
[11, 360] loss: 0.330
Epoch: 11 -> Loss: 0.320974588394
Epoch: 11 -> Test Accuracy: 86.18
[12, 60] loss: 0.280
[12, 120] loss: 0.280
[12, 180] loss: 0.281
[12, 240] loss: 0.292
[12, 300] loss: 0.298
[12, 360] loss: 0.311
Epoch: 12 -> Loss: 0.311203598976
Epoch: 12 -> Test Accuracy: 85.38
[13, 60] loss: 0.266
[13, 120] loss: 0.289
[13, 180] loss: 0.293
[13, 240] loss: 0.303
[13, 300] loss: 0.298
[13, 360] loss: 0.304
Epoch: 13 -> Loss: 0.38963535428
Epoch: 13 -> Test Accuracy: 85.49
[14, 60] loss: 0.279
[14, 120] loss: 0.287
[14, 180] loss: 0.293
[14, 240] loss: 0.276
[14, 300] loss: 0.309
[14, 360] loss: 0.304
Epoch: 14 -> Loss: 0.336613625288
Epoch: 14 -> Test Accuracy: 85.05
[15, 60] loss: 0.260
[15, 120] loss: 0.287
[15, 180] loss: 0.293
[15, 240] loss: 0.281
[15, 300] loss: 0.278
[15, 360] loss: 0.284
Epoch: 15 -> Loss: 0.39779239893
Epoch: 15 -> Test Accuracy: 84.99
[16, 60] loss: 0.274
[16, 120] loss: 0.273
[16, 180] loss: 0.269
[16, 240] loss: 0.287
[16, 300] loss: 0.298
[16, 360] loss: 0.305
Epoch: 16 -> Loss: 0.339841604233
Epoch: 16 -> Test Accuracy: 85.91
[17, 60] loss: 0.256
[17, 120] loss: 0.273
[17, 180] loss: 0.294
[17, 240] loss: 0.283
[17, 300] loss: 0.304
[17, 360] loss: 0.299
Epoch: 17 -> Loss: 0.359031558037
Epoch: 17 -> Test Accuracy: 85.61
[18, 60] loss: 0.258
[18, 120] loss: 0.259
[18, 180] loss: 0.273
[18, 240] loss: 0.285
[18, 300] loss: 0.288
[18, 360] loss: 0.306
Epoch: 18 -> Loss: 0.35723093152
Epoch: 18 -> Test Accuracy: 85.08
[19, 60] loss: 0.284
[19, 120] loss: 0.280
[19, 180] loss: 0.278
[19, 240] loss: 0.276
[19, 300] loss: 0.277
[19, 360] loss: 0.288
Epoch: 19 -> Loss: 0.2690769732
Epoch: 19 -> Test Accuracy: 85.38
[20, 60] loss: 0.261
[20, 120] loss: 0.256
[20, 180] loss: 0.284
[20, 240] loss: 0.284
[20, 300] loss: 0.278
[20, 360] loss: 0.301
Epoch: 20 -> Loss: 0.276969313622
Epoch: 20 -> Test Accuracy: 86.02
[21, 60] loss: 0.264
[21, 120] loss: 0.265
[21, 180] loss: 0.252
[21, 240] loss: 0.278
[21, 300] loss: 0.289
[21, 360] loss: 0.303
Epoch: 21 -> Loss: 0.314692467451
Epoch: 21 -> Test Accuracy: 85.43
[22, 60] loss: 0.257
[22, 120] loss: 0.264
[22, 180] loss: 0.274
[22, 240] loss: 0.277
[22, 300] loss: 0.281
[22, 360] loss: 0.284
Epoch: 22 -> Loss: 0.241112902761
Epoch: 22 -> Test Accuracy: 85.38
[23, 60] loss: 0.250
[23, 120] loss: 0.259
[23, 180] loss: 0.265
[23, 240] loss: 0.287
[23, 300] loss: 0.267
[23, 360] loss: 0.303
Epoch: 23 -> Loss: 0.45448166132
Epoch: 23 -> Test Accuracy: 85.46
[24, 60] loss: 0.241
[24, 120] loss: 0.261
[24, 180] loss: 0.277
[24, 240] loss: 0.274
[24, 300] loss: 0.285
[24, 360] loss: 0.289
Epoch: 24 -> Loss: 0.220754593611
Epoch: 24 -> Test Accuracy: 85.4
[25, 60] loss: 0.256
[25, 120] loss: 0.249
[25, 180] loss: 0.258
[25, 240] loss: 0.273
[25, 300] loss: 0.278
[25, 360] loss: 0.304
Epoch: 25 -> Loss: 0.273232907057
Epoch: 25 -> Test Accuracy: 85.32
[26, 60] loss: 0.250
[26, 120] loss: 0.254
[26, 180] loss: 0.270
[26, 240] loss: 0.270
[26, 300] loss: 0.286
[26, 360] loss: 0.270
Epoch: 26 -> Loss: 0.186416223645
Epoch: 26 -> Test Accuracy: 86.0
[27, 60] loss: 0.246
[27, 120] loss: 0.254
[27, 180] loss: 0.262
[27, 240] loss: 0.277
[27, 300] loss: 0.294
[27, 360] loss: 0.298
Epoch: 27 -> Loss: 0.315597355366
Epoch: 27 -> Test Accuracy: 85.52
[28, 60] loss: 0.252
[28, 120] loss: 0.255
[28, 180] loss: 0.255
[28, 240] loss: 0.277
[28, 300] loss: 0.280
[28, 360] loss: 0.274
Epoch: 28 -> Loss: 0.28716173768
Epoch: 28 -> Test Accuracy: 86.13
[29, 60] loss: 0.251
[29, 120] loss: 0.249
[29, 180] loss: 0.275
[29, 240] loss: 0.275
[29, 300] loss: 0.293
[29, 360] loss: 0.266
Epoch: 29 -> Loss: 0.236902907491
Epoch: 29 -> Test Accuracy: 86.14
[30, 60] loss: 0.242
[30, 120] loss: 0.254
[30, 180] loss: 0.268
[30, 240] loss: 0.270
[30, 300] loss: 0.285
[30, 360] loss: 0.262
Epoch: 30 -> Loss: 0.310633897781
Epoch: 30 -> Test Accuracy: 85.46
[31, 60] loss: 0.252
[31, 120] loss: 0.249
[31, 180] loss: 0.265
[31, 240] loss: 0.274
[31, 300] loss: 0.278
[31, 360] loss: 0.285
Epoch: 31 -> Loss: 0.324928581715
Epoch: 31 -> Test Accuracy: 86.2
[32, 60] loss: 0.248
[32, 120] loss: 0.260
[32, 180] loss: 0.277
[32, 240] loss: 0.266
[32, 300] loss: 0.275
[32, 360] loss: 0.281
Epoch: 32 -> Loss: 0.217931956053
Epoch: 32 -> Test Accuracy: 85.69
[33, 60] loss: 0.234
[33, 120] loss: 0.257
[33, 180] loss: 0.261
[33, 240] loss: 0.263
[33, 300] loss: 0.284
[33, 360] loss: 0.290
Epoch: 33 -> Loss: 0.226124957204
Epoch: 33 -> Test Accuracy: 85.89
[34, 60] loss: 0.234
[34, 120] loss: 0.247
[34, 180] loss: 0.266
[34, 240] loss: 0.272
[34, 300] loss: 0.284
[34, 360] loss: 0.280
Epoch: 34 -> Loss: 0.312292128801
Epoch: 34 -> Test Accuracy: 85.61
[35, 60] loss: 0.239
[35, 120] loss: 0.258
[35, 180] loss: 0.267
[35, 240] loss: 0.266
[35, 300] loss: 0.276
[35, 360] loss: 0.288
Epoch: 35 -> Loss: 0.232284829021
Epoch: 35 -> Test Accuracy: 86.26
[36, 60] loss: 0.203
[36, 120] loss: 0.188
[36, 180] loss: 0.181
[36, 240] loss: 0.160
[36, 300] loss: 0.169
[36, 360] loss: 0.176
Epoch: 36 -> Loss: 0.147477537394
Epoch: 36 -> Test Accuracy: 88.2
[37, 60] loss: 0.141
[37, 120] loss: 0.147
[37, 180] loss: 0.145
[37, 240] loss: 0.147
[37, 300] loss: 0.150
[37, 360] loss: 0.148
Epoch: 37 -> Loss: 0.127799779177
Epoch: 37 -> Test Accuracy: 88.26
[38, 60] loss: 0.130
[38, 120] loss: 0.131
[38, 180] loss: 0.138
[38, 240] loss: 0.145
[38, 300] loss: 0.137
[38, 360] loss: 0.143
Epoch: 38 -> Loss: 0.101877167821
Epoch: 38 -> Test Accuracy: 88.04
[39, 60] loss: 0.117
[39, 120] loss: 0.120
[39, 180] loss: 0.125
[39, 240] loss: 0.116
[39, 300] loss: 0.135
[39, 360] loss: 0.136
Epoch: 39 -> Loss: 0.118559643626
Epoch: 39 -> Test Accuracy: 88.24
[40, 60] loss: 0.113
[40, 120] loss: 0.117
[40, 180] loss: 0.118
[40, 240] loss: 0.120
[40, 300] loss: 0.124
[40, 360] loss: 0.123
Epoch: 40 -> Loss: 0.0976566374302
Epoch: 40 -> Test Accuracy: 88.12
[41, 60] loss: 0.113
[41, 120] loss: 0.112
[41, 180] loss: 0.115
[41, 240] loss: 0.108
[41, 300] loss: 0.119
[41, 360] loss: 0.122
Epoch: 41 -> Loss: 0.0460130013525
Epoch: 41 -> Test Accuracy: 87.94
[42, 60] loss: 0.100
[42, 120] loss: 0.103
[42, 180] loss: 0.112
[42, 240] loss: 0.111
[42, 300] loss: 0.116
[42, 360] loss: 0.119
Epoch: 42 -> Loss: 0.0915532708168
Epoch: 42 -> Test Accuracy: 88.0
[43, 60] loss: 0.093
[43, 120] loss: 0.104
[43, 180] loss: 0.107
[43, 240] loss: 0.107
[43, 300] loss: 0.120
[43, 360] loss: 0.121
Epoch: 43 -> Loss: 0.0771202594042
Epoch: 43 -> Test Accuracy: 87.67
[44, 60] loss: 0.103
[44, 120] loss: 0.101
[44, 180] loss: 0.104
[44, 240] loss: 0.109
[44, 300] loss: 0.111
[44, 360] loss: 0.122
Epoch: 44 -> Loss: 0.1015945822
Epoch: 44 -> Test Accuracy: 88.09
[45, 60] loss: 0.098
[45, 120] loss: 0.104
[45, 180] loss: 0.102
[45, 240] loss: 0.109
[45, 300] loss: 0.119
[45, 360] loss: 0.109
Epoch: 45 -> Loss: 0.143879905343
Epoch: 45 -> Test Accuracy: 88.02
[46, 60] loss: 0.092
[46, 120] loss: 0.103
[46, 180] loss: 0.105
[46, 240] loss: 0.113
[46, 300] loss: 0.104
[46, 360] loss: 0.113
Epoch: 46 -> Loss: 0.152754217386
Epoch: 46 -> Test Accuracy: 87.93
[47, 60] loss: 0.100
[47, 120] loss: 0.107
[47, 180] loss: 0.110
[47, 240] loss: 0.109
[47, 300] loss: 0.113
[47, 360] loss: 0.117
Epoch: 47 -> Loss: 0.180139839649
Epoch: 47 -> Test Accuracy: 87.57
[48, 60] loss: 0.093
[48, 120] loss: 0.100
[48, 180] loss: 0.095
[48, 240] loss: 0.121
[48, 300] loss: 0.111
[48, 360] loss: 0.114
Epoch: 48 -> Loss: 0.161243930459
Epoch: 48 -> Test Accuracy: 87.3
[49, 60] loss: 0.095
[49, 120] loss: 0.099
[49, 180] loss: 0.109
[49, 240] loss: 0.107
[49, 300] loss: 0.120
[49, 360] loss: 0.119
Epoch: 49 -> Loss: 0.136429190636
Epoch: 49 -> Test Accuracy: 87.67
[50, 60] loss: 0.094
[50, 120] loss: 0.104
[50, 180] loss: 0.102
[50, 240] loss: 0.105
[50, 300] loss: 0.113
[50, 360] loss: 0.120
Epoch: 50 -> Loss: 0.105074964464
Epoch: 50 -> Test Accuracy: 87.79
[51, 60] loss: 0.102
[51, 120] loss: 0.106
[51, 180] loss: 0.109
[51, 240] loss: 0.103
[51, 300] loss: 0.108
[51, 360] loss: 0.122
Epoch: 51 -> Loss: 0.130261033773
Epoch: 51 -> Test Accuracy: 87.58
[52, 60] loss: 0.099
[52, 120] loss: 0.096
[52, 180] loss: 0.109
[52, 240] loss: 0.105
[52, 300] loss: 0.123
[52, 360] loss: 0.120
Epoch: 52 -> Loss: 0.105667151511
Epoch: 52 -> Test Accuracy: 87.7
[53, 60] loss: 0.100
[53, 120] loss: 0.106
[53, 180] loss: 0.101
[53, 240] loss: 0.113
[53, 300] loss: 0.116
[53, 360] loss: 0.127
Epoch: 53 -> Loss: 0.101517722011
Epoch: 53 -> Test Accuracy: 87.69
[54, 60] loss: 0.103
[54, 120] loss: 0.101
[54, 180] loss: 0.111
[54, 240] loss: 0.111
[54, 300] loss: 0.115
[54, 360] loss: 0.109
Epoch: 54 -> Loss: 0.0614903457463
Epoch: 54 -> Test Accuracy: 87.23
[55, 60] loss: 0.097
[55, 120] loss: 0.096
[55, 180] loss: 0.104
[55, 240] loss: 0.109
[55, 300] loss: 0.121
[55, 360] loss: 0.121
Epoch: 55 -> Loss: 0.0488305650651
Epoch: 55 -> Test Accuracy: 87.71
[56, 60] loss: 0.106
[56, 120] loss: 0.105
[56, 180] loss: 0.109
[56, 240] loss: 0.105
[56, 300] loss: 0.113
[56, 360] loss: 0.121
Epoch: 56 -> Loss: 0.179651007056
Epoch: 56 -> Test Accuracy: 87.39
[57, 60] loss: 0.105
[57, 120] loss: 0.108
[57, 180] loss: 0.112
[57, 240] loss: 0.114
[57, 300] loss: 0.104
[57, 360] loss: 0.107
Epoch: 57 -> Loss: 0.133594423532
Epoch: 57 -> Test Accuracy: 87.06
[58, 60] loss: 0.105
[58, 120] loss: 0.103
[58, 180] loss: 0.106
[58, 240] loss: 0.104
[58, 300] loss: 0.116
[58, 360] loss: 0.119
Epoch: 58 -> Loss: 0.113112285733
Epoch: 58 -> Test Accuracy: 87.46
[59, 60] loss: 0.100
[59, 120] loss: 0.105
[59, 180] loss: 0.112
[59, 240] loss: 0.110
[59, 300] loss: 0.108
[59, 360] loss: 0.123
Epoch: 59 -> Loss: 0.120920315385
Epoch: 59 -> Test Accuracy: 87.41
[60, 60] loss: 0.102
[60, 120] loss: 0.108
[60, 180] loss: 0.110
[60, 240] loss: 0.107
[60, 300] loss: 0.115
[60, 360] loss: 0.113
Epoch: 60 -> Loss: 0.128071188927
Epoch: 60 -> Test Accuracy: 86.91
[61, 60] loss: 0.109
[61, 120] loss: 0.100
[61, 180] loss: 0.107
[61, 240] loss: 0.116
[61, 300] loss: 0.110
[61, 360] loss: 0.108
Epoch: 61 -> Loss: 0.24754679203
Epoch: 61 -> Test Accuracy: 87.13
[62, 60] loss: 0.099
[62, 120] loss: 0.099
[62, 180] loss: 0.098
[62, 240] loss: 0.107
[62, 300] loss: 0.117
[62, 360] loss: 0.123
Epoch: 62 -> Loss: 0.140629321337
Epoch: 62 -> Test Accuracy: 87.19
[63, 60] loss: 0.099
[63, 120] loss: 0.109
[63, 180] loss: 0.096
[63, 240] loss: 0.106
[63, 300] loss: 0.112
[63, 360] loss: 0.113
Epoch: 63 -> Loss: 0.116040453315
Epoch: 63 -> Test Accuracy: 87.03
[64, 60] loss: 0.099
[64, 120] loss: 0.091
[64, 180] loss: 0.098
[64, 240] loss: 0.107
[64, 300] loss: 0.116
[64, 360] loss: 0.118
Epoch: 64 -> Loss: 0.0881711989641
Epoch: 64 -> Test Accuracy: 87.67
[65, 60] loss: 0.094
[65, 120] loss: 0.101
[65, 180] loss: 0.103
[65, 240] loss: 0.104
[65, 300] loss: 0.101
[65, 360] loss: 0.114
Epoch: 65 -> Loss: 0.0817269459367
Epoch: 65 -> Test Accuracy: 87.36
[66, 60] loss: 0.097
[66, 120] loss: 0.106
[66, 180] loss: 0.102
[66, 240] loss: 0.109
[66, 300] loss: 0.111
[66, 360] loss: 0.114
Epoch: 66 -> Loss: 0.164013296366
Epoch: 66 -> Test Accuracy: 87.58
[67, 60] loss: 0.103
[67, 120] loss: 0.105
[67, 180] loss: 0.103
[67, 240] loss: 0.114
[67, 300] loss: 0.108
[67, 360] loss: 0.114
Epoch: 67 -> Loss: 0.0772556811571
Epoch: 67 -> Test Accuracy: 86.82
[68, 60] loss: 0.100
[68, 120] loss: 0.103
[68, 180] loss: 0.108
[68, 240] loss: 0.100
[68, 300] loss: 0.110
[68, 360] loss: 0.113
Epoch: 68 -> Loss: 0.0822162479162
Epoch: 68 -> Test Accuracy: 87.56
[69, 60] loss: 0.097
[69, 120] loss: 0.098
[69, 180] loss: 0.101
[69, 240] loss: 0.104
[69, 300] loss: 0.116
[69, 360] loss: 0.111
Epoch: 69 -> Loss: 0.102196291089
Epoch: 69 -> Test Accuracy: 87.01
[70, 60] loss: 0.100
[70, 120] loss: 0.093
[70, 180] loss: 0.108
[70, 240] loss: 0.109
[70, 300] loss: 0.104
[70, 360] loss: 0.114
Epoch: 70 -> Loss: 0.189903616905
Epoch: 70 -> Test Accuracy: 87.45
[71, 60] loss: 0.074
[71, 120] loss: 0.071
[71, 180] loss: 0.068
[71, 240] loss: 0.064
[71, 300] loss: 0.062
[71, 360] loss: 0.062
Epoch: 71 -> Loss: 0.0460370704532
Epoch: 71 -> Test Accuracy: 88.58
[72, 60] loss: 0.054
[72, 120] loss: 0.056
[72, 180] loss: 0.053
[72, 240] loss: 0.050
[72, 300] loss: 0.060
[72, 360] loss: 0.056
Epoch: 72 -> Loss: 0.0404352359474
Epoch: 72 -> Test Accuracy: 88.54
[73, 60] loss: 0.049
[73, 120] loss: 0.044
[73, 180] loss: 0.052
[73, 240] loss: 0.051
[73, 300] loss: 0.051
[73, 360] loss: 0.048
Epoch: 73 -> Loss: 0.0345988273621
Epoch: 73 -> Test Accuracy: 88.76
[74, 60] loss: 0.042
[74, 120] loss: 0.047
[74, 180] loss: 0.045
[74, 240] loss: 0.048
[74, 300] loss: 0.047
[74, 360] loss: 0.042
Epoch: 74 -> Loss: 0.020695855841
Epoch: 74 -> Test Accuracy: 88.74
[75, 60] loss: 0.043
[75, 120] loss: 0.044
[75, 180] loss: 0.041
[75, 240] loss: 0.044
[75, 300] loss: 0.044
[75, 360] loss: 0.045
Epoch: 75 -> Loss: 0.0674355328083
Epoch: 75 -> Test Accuracy: 88.52
[76, 60] loss: 0.043
[76, 120] loss: 0.041
[76, 180] loss: 0.038
[76, 240] loss: 0.039
[76, 300] loss: 0.042
[76, 360] loss: 0.043
Epoch: 76 -> Loss: 0.0397047698498
Epoch: 76 -> Test Accuracy: 88.69
[77, 60] loss: 0.041
[77, 120] loss: 0.041
[77, 180] loss: 0.037
[77, 240] loss: 0.041
[77, 300] loss: 0.044
[77, 360] loss: 0.040
Epoch: 77 -> Loss: 0.0739384442568
Epoch: 77 -> Test Accuracy: 88.88
[78, 60] loss: 0.036
[78, 120] loss: 0.038
[78, 180] loss: 0.041
[78, 240] loss: 0.040
[78, 300] loss: 0.039
[78, 360] loss: 0.038
Epoch: 78 -> Loss: 0.0402158088982
Epoch: 78 -> Test Accuracy: 88.58
[79, 60] loss: 0.037
[79, 120] loss: 0.041
[79, 180] loss: 0.035
[79, 240] loss: 0.037
[79, 300] loss: 0.037
[79, 360] loss: 0.037
Epoch: 79 -> Loss: 0.0498256273568
Epoch: 79 -> Test Accuracy: 88.7
[80, 60] loss: 0.037
[80, 120] loss: 0.035
[80, 180] loss: 0.037
[80, 240] loss: 0.035
[80, 300] loss: 0.040
[80, 360] loss: 0.037
Epoch: 80 -> Loss: 0.0296311341226
Epoch: 80 -> Test Accuracy: 88.63
[81, 60] loss: 0.035
[81, 120] loss: 0.036
[81, 180] loss: 0.039
[81, 240] loss: 0.037
[81, 300] loss: 0.034
[81, 360] loss: 0.035
Epoch: 81 -> Loss: 0.050663150847
Epoch: 81 -> Test Accuracy: 88.52
[82, 60] loss: 0.033
[82, 120] loss: 0.031
[82, 180] loss: 0.035
[82, 240] loss: 0.035
[82, 300] loss: 0.038
[82, 360] loss: 0.035
Epoch: 82 -> Loss: 0.021719366312
Epoch: 82 -> Test Accuracy: 88.72
[83, 60] loss: 0.032
[83, 120] loss: 0.035
[83, 180] loss: 0.037
[83, 240] loss: 0.033
[83, 300] loss: 0.034
[83, 360] loss: 0.034
Epoch: 83 -> Loss: 0.021537065506
Epoch: 83 -> Test Accuracy: 88.86
[84, 60] loss: 0.031
[84, 120] loss: 0.034
[84, 180] loss: 0.036
[84, 240] loss: 0.036
[84, 300] loss: 0.034
[84, 360] loss: 0.032
Epoch: 84 -> Loss: 0.0686306804419
Epoch: 84 -> Test Accuracy: 88.78
[85, 60] loss: 0.032
[85, 120] loss: 0.032
[85, 180] loss: 0.032
[85, 240] loss: 0.032
[85, 300] loss: 0.032
[85, 360] loss: 0.032
Epoch: 85 -> Loss: 0.038092110306
Epoch: 85 -> Test Accuracy: 88.81
[86, 60] loss: 0.029
[86, 120] loss: 0.030
[86, 180] loss: 0.029
[86, 240] loss: 0.029
[86, 300] loss: 0.029
[86, 360] loss: 0.029
Epoch: 86 -> Loss: 0.0370008982718
Epoch: 86 -> Test Accuracy: 88.88
[87, 60] loss: 0.029
[87, 120] loss: 0.027
[87, 180] loss: 0.028
[87, 240] loss: 0.027
[87, 300] loss: 0.031
[87, 360] loss: 0.030
Epoch: 87 -> Loss: 0.0638825148344
Epoch: 87 -> Test Accuracy: 88.99
[88, 60] loss: 0.030
[88, 120] loss: 0.028
[88, 180] loss: 0.027
[88, 240] loss: 0.028
[88, 300] loss: 0.025
[88, 360] loss: 0.028
Epoch: 88 -> Loss: 0.0306285060942
Epoch: 88 -> Test Accuracy: 88.68
[89, 60] loss: 0.027
[89, 120] loss: 0.027
[89, 180] loss: 0.029
[89, 240] loss: 0.030
[89, 300] loss: 0.028
[89, 360] loss: 0.030
Epoch: 89 -> Loss: 0.0529460385442
Epoch: 89 -> Test Accuracy: 88.88
[90, 60] loss: 0.027
[90, 120] loss: 0.028
[90, 180] loss: 0.029
[90, 240] loss: 0.029
[90, 300] loss: 0.029
[90, 360] loss: 0.025
Epoch: 90 -> Loss: 0.0197838954628
Epoch: 90 -> Test Accuracy: 88.89
[91, 60] loss: 0.026
[91, 120] loss: 0.029
[91, 180] loss: 0.028
[91, 240] loss: 0.028
[91, 300] loss: 0.026
[91, 360] loss: 0.026
Epoch: 91 -> Loss: 0.024331022054
Epoch: 91 -> Test Accuracy: 88.9
[92, 60] loss: 0.027
[92, 120] loss: 0.028
[92, 180] loss: 0.026
[92, 240] loss: 0.026
[92, 300] loss: 0.025
[92, 360] loss: 0.029
Epoch: 92 -> Loss: 0.0443188846111
Epoch: 92 -> Test Accuracy: 88.86
[93, 60] loss: 0.025
[93, 120] loss: 0.027
[93, 180] loss: 0.028
[93, 240] loss: 0.027
[93, 300] loss: 0.029
[93, 360] loss: 0.029
Epoch: 93 -> Loss: 0.0265762563795
Epoch: 93 -> Test Accuracy: 88.8
[94, 60] loss: 0.026
[94, 120] loss: 0.027
[94, 180] loss: 0.027
[94, 240] loss: 0.026
[94, 300] loss: 0.027
[94, 360] loss: 0.026
Epoch: 94 -> Loss: 0.0204644501209
Epoch: 94 -> Test Accuracy: 88.72
[95, 60] loss: 0.025
[95, 120] loss: 0.027
[95, 180] loss: 0.028
[95, 240] loss: 0.028
[95, 300] loss: 0.027
[95, 360] loss: 0.027
Epoch: 95 -> Loss: 0.0251078605652
Epoch: 95 -> Test Accuracy: 88.82
[96, 60] loss: 0.025
[96, 120] loss: 0.025
[96, 180] loss: 0.028
[96, 240] loss: 0.025
[96, 300] loss: 0.027
[96, 360] loss: 0.027
Epoch: 96 -> Loss: 0.0565241165459
Epoch: 96 -> Test Accuracy: 88.9
[97, 60] loss: 0.029
[97, 120] loss: 0.026
[97, 180] loss: 0.025
[97, 240] loss: 0.027
[97, 300] loss: 0.027
[97, 360] loss: 0.026
Epoch: 97 -> Loss: 0.0237293429673
Epoch: 97 -> Test Accuracy: 88.82
[98, 60] loss: 0.023
[98, 120] loss: 0.025
[98, 180] loss: 0.027
[98, 240] loss: 0.026
[98, 300] loss: 0.025
[98, 360] loss: 0.027
Epoch: 98 -> Loss: 0.0092001138255
Epoch: 98 -> Test Accuracy: 88.76
[99, 60] loss: 0.024
[99, 120] loss: 0.029
[99, 180] loss: 0.025
[99, 240] loss: 0.025
[99, 300] loss: 0.026
[99, 360] loss: 0.027
Epoch: 99 -> Loss: 0.0157920122147
Epoch: 99 -> Test Accuracy: 88.78
[100, 60] loss: 0.025
[100, 120] loss: 0.025
[100, 180] loss: 0.025
[100, 240] loss: 0.025
[100, 300] loss: 0.026
[100, 360] loss: 0.024
Epoch: 100 -> Loss: 0.0201563090086
Epoch: 100 -> Test Accuracy: 88.82
Finished Training
[1, 60] loss: 1.863
[1, 120] loss: 1.657
[1, 180] loss: 1.573
[1, 240] loss: 1.547
[1, 300] loss: 1.518
[1, 360] loss: 1.465
Epoch: 1 -> Loss: 1.38100779057
Epoch: 1 -> Test Accuracy: 42.51
[2, 60] loss: 1.455
[2, 120] loss: 1.455
[2, 180] loss: 1.424
[2, 240] loss: 1.439
[2, 300] loss: 1.431
[2, 360] loss: 1.394
Epoch: 2 -> Loss: 1.57546544075
Epoch: 2 -> Test Accuracy: 46.54
[3, 60] loss: 1.372
[3, 120] loss: 1.386
[3, 180] loss: 1.390
[3, 240] loss: 1.368
[3, 300] loss: 1.359
[3, 360] loss: 1.359
Epoch: 3 -> Loss: 1.44619822502
Epoch: 3 -> Test Accuracy: 46.99
[4, 60] loss: 1.343
[4, 120] loss: 1.357
[4, 180] loss: 1.338
[4, 240] loss: 1.347
[4, 300] loss: 1.311
[4, 360] loss: 1.337
Epoch: 4 -> Loss: 1.35329127312
Epoch: 4 -> Test Accuracy: 48.98
[5, 60] loss: 1.329
[5, 120] loss: 1.306
[5, 180] loss: 1.312
[5, 240] loss: 1.317
[5, 300] loss: 1.333
[5, 360] loss: 1.317
Epoch: 5 -> Loss: 1.33428633213
Epoch: 5 -> Test Accuracy: 48.82
[6, 60] loss: 1.311
[6, 120] loss: 1.305
[6, 180] loss: 1.277
[6, 240] loss: 1.309
[6, 300] loss: 1.308
[6, 360] loss: 1.283
Epoch: 6 -> Loss: 1.38868832588
Epoch: 6 -> Test Accuracy: 48.68
[7, 60] loss: 1.287
[7, 120] loss: 1.294
[7, 180] loss: 1.272
[7, 240] loss: 1.288
[7, 300] loss: 1.297
[7, 360] loss: 1.285
Epoch: 7 -> Loss: 1.12669825554
Epoch: 7 -> Test Accuracy: 49.95
[8, 60] loss: 1.289
[8, 120] loss: 1.275
[8, 180] loss: 1.278
[8, 240] loss: 1.263
[8, 300] loss: 1.283
[8, 360] loss: 1.283
Epoch: 8 -> Loss: 1.37220048904
Epoch: 8 -> Test Accuracy: 50.36
[9, 60] loss: 1.268
[9, 120] loss: 1.257
[9, 180] loss: 1.257
[9, 240] loss: 1.270
[9, 300] loss: 1.275
[9, 360] loss: 1.271
Epoch: 9 -> Loss: 1.19314074516
Epoch: 9 -> Test Accuracy: 49.89
[10, 60] loss: 1.278
[10, 120] loss: 1.246
[10, 180] loss: 1.270
[10, 240] loss: 1.252
[10, 300] loss: 1.265
[10, 360] loss: 1.269
Epoch: 10 -> Loss: 1.18051970005
Epoch: 10 -> Test Accuracy: 50.67
[11, 60] loss: 1.248
[11, 120] loss: 1.282
[11, 180] loss: 1.244
[11, 240] loss: 1.267
[11, 300] loss: 1.260
[11, 360] loss: 1.241
Epoch: 11 -> Loss: 1.45489859581
Epoch: 11 -> Test Accuracy: 50.52
[12, 60] loss: 1.276
[12, 120] loss: 1.252
[12, 180] loss: 1.259
[12, 240] loss: 1.249
[12, 300] loss: 1.243
[12, 360] loss: 1.251
Epoch: 12 -> Loss: 1.29271221161
Epoch: 12 -> Test Accuracy: 49.86
[13, 60] loss: 1.252
[13, 120] loss: 1.256
[13, 180] loss: 1.255
[13, 240] loss: 1.249
[13, 300] loss: 1.249
[13, 360] loss: 1.262
Epoch: 13 -> Loss: 1.41856431961
Epoch: 13 -> Test Accuracy: 50.32
[14, 60] loss: 1.262
[14, 120] loss: 1.239
[14, 180] loss: 1.260
[14, 240] loss: 1.256
[14, 300] loss: 1.254
[14, 360] loss: 1.233
Epoch: 14 -> Loss: 1.20106911659
Epoch: 14 -> Test Accuracy: 49.65
[15, 60] loss: 1.250
[15, 120] loss: 1.250
[15, 180] loss: 1.234
[15, 240] loss: 1.224
[15, 300] loss: 1.250
[15, 360] loss: 1.235
Epoch: 15 -> Loss: 1.21732747555
Epoch: 15 -> Test Accuracy: 51.29
[16, 60] loss: 1.247
[16, 120] loss: 1.246
[16, 180] loss: 1.217
[16, 240] loss: 1.224
[16, 300] loss: 1.253
[16, 360] loss: 1.238
Epoch: 16 -> Loss: 1.28469765186
Epoch: 16 -> Test Accuracy: 51.23
[17, 60] loss: 1.226
[17, 120] loss: 1.239
[17, 180] loss: 1.248
[17, 240] loss: 1.240
[17, 300] loss: 1.244
[17, 360] loss: 1.251
Epoch: 17 -> Loss: 1.24963212013
Epoch: 17 -> Test Accuracy: 51.43
[18, 60] loss: 1.245
[18, 120] loss: 1.241
[18, 180] loss: 1.260
[18, 240] loss: 1.241
[18, 300] loss: 1.249
[18, 360] loss: 1.231
Epoch: 18 -> Loss: 1.35986924171
Epoch: 18 -> Test Accuracy: 50.88
[19, 60] loss: 1.251
[19, 120] loss: 1.233
[19, 180] loss: 1.229
[19, 240] loss: 1.237
[19, 300] loss: 1.233
[19, 360] loss: 1.231
Epoch: 19 -> Loss: 1.27556943893
Epoch: 19 -> Test Accuracy: 50.71
[20, 60] loss: 1.236
[20, 120] loss: 1.227
[20, 180] loss: 1.237
[20, 240] loss: 1.235
[20, 300] loss: 1.200
[20, 360] loss: 1.246
Epoch: 20 -> Loss: 1.04955816269
Epoch: 20 -> Test Accuracy: 50.77
[21, 60] loss: 1.234
[21, 120] loss: 1.219
[21, 180] loss: 1.241
[21, 240] loss: 1.230
[21, 300] loss: 1.253
[21, 360] loss: 1.221
Epoch: 21 -> Loss: 1.27590477467
Epoch: 21 -> Test Accuracy: 51.65
[22, 60] loss: 1.239
[22, 120] loss: 1.219
[22, 180] loss: 1.216
[22, 240] loss: 1.223
[22, 300] loss: 1.239
[22, 360] loss: 1.237
Epoch: 22 -> Loss: 1.14897048473
Epoch: 22 -> Test Accuracy: 52.7
[23, 60] loss: 1.232
[23, 120] loss: 1.231
[23, 180] loss: 1.215
[23, 240] loss: 1.230
[23, 300] loss: 1.220
[23, 360] loss: 1.253
Epoch: 23 -> Loss: 1.38017296791
Epoch: 23 -> Test Accuracy: 51.58
[24, 60] loss: 1.223
[24, 120] loss: 1.242
[24, 180] loss: 1.219
[24, 240] loss: 1.231
[24, 300] loss: 1.230
[24, 360] loss: 1.232
Epoch: 24 -> Loss: 1.33723008633
Epoch: 24 -> Test Accuracy: 50.85
[25, 60] loss: 1.218
[25, 120] loss: 1.220
[25, 180] loss: 1.244
[25, 240] loss: 1.210
[25, 300] loss: 1.245
[25, 360] loss: 1.240
Epoch: 25 -> Loss: 1.19878721237
Epoch: 25 -> Test Accuracy: 50.58
[26, 60] loss: 1.242
[26, 120] loss: 1.240
[26, 180] loss: 1.208
[26, 240] loss: 1.222
[26, 300] loss: 1.225
[26, 360] loss: 1.220
Epoch: 26 -> Loss: 1.30900859833
Epoch: 26 -> Test Accuracy: 52.41
[27, 60] loss: 1.221
[27, 120] loss: 1.225
[27, 180] loss: 1.224
[27, 240] loss: 1.233
[27, 300] loss: 1.218
[27, 360] loss: 1.201
Epoch: 27 -> Loss: 1.29304289818
Epoch: 27 -> Test Accuracy: 51.48
[28, 60] loss: 1.235
[28, 120] loss: 1.200
[28, 180] loss: 1.212
[28, 240] loss: 1.229
[28, 300] loss: 1.252
[28, 360] loss: 1.250
Epoch: 28 -> Loss: 1.21757674217
Epoch: 28 -> Test Accuracy: 50.61
[29, 60] loss: 1.236
[29, 120] loss: 1.219
[29, 180] loss: 1.221
[29, 240] loss: 1.235
[29, 300] loss: 1.239
[29, 360] loss: 1.212
Epoch: 29 -> Loss: 1.29875969887
Epoch: 29 -> Test Accuracy: 51.41
[30, 60] loss: 1.200
[30, 120] loss: 1.228
[30, 180] loss: 1.215
[30, 240] loss: 1.227
[30, 300] loss: 1.237
[30, 360] loss: 1.239
Epoch: 30 -> Loss: 1.42523491383
Epoch: 30 -> Test Accuracy: 52.18
[31, 60] loss: 1.216
[31, 120] loss: 1.223
[31, 180] loss: 1.213
[31, 240] loss: 1.239
[31, 300] loss: 1.220
[31, 360] loss: 1.222
Epoch: 31 -> Loss: 1.45415902138
Epoch: 31 -> Test Accuracy: 51.43
[32, 60] loss: 1.229
[32, 120] loss: 1.199
[32, 180] loss: 1.220
[32, 240] loss: 1.234
[32, 300] loss: 1.241
[32, 360] loss: 1.227
Epoch: 32 -> Loss: 1.24754357338
Epoch: 32 -> Test Accuracy: 50.31
[33, 60] loss: 1.212
[33, 120] loss: 1.217
[33, 180] loss: 1.222
[33, 240] loss: 1.230
[33, 300] loss: 1.222
[33, 360] loss: 1.223
Epoch: 33 -> Loss: 1.21123230457
Epoch: 33 -> Test Accuracy: 52.15
[34, 60] loss: 1.231
[34, 120] loss: 1.237
[34, 180] loss: 1.204
[34, 240] loss: 1.231
[34, 300] loss: 1.209
[34, 360] loss: 1.218
Epoch: 34 -> Loss: 1.27467799187
Epoch: 34 -> Test Accuracy: 51.37
[35, 60] loss: 1.201
[35, 120] loss: 1.208
[35, 180] loss: 1.224
[35, 240] loss: 1.230
[35, 300] loss: 1.244
[35, 360] loss: 1.226
Epoch: 35 -> Loss: 1.30367970467
Epoch: 35 -> Test Accuracy: 51.49
[36, 60] loss: 1.162
[36, 120] loss: 1.125
[36, 180] loss: 1.105
[36, 240] loss: 1.097
[36, 300] loss: 1.100
[36, 360] loss: 1.081
Epoch: 36 -> Loss: 1.13254475594
Epoch: 36 -> Test Accuracy: 56.02
[37, 60] loss: 1.088
[37, 120] loss: 1.099
[37, 180] loss: 1.086
[37, 240] loss: 1.068
[37, 300] loss: 1.099
[37, 360] loss: 1.099
Epoch: 37 -> Loss: 1.07157111168
Epoch: 37 -> Test Accuracy: 56.53
[38, 60] loss: 1.071
[38, 120] loss: 1.076
[38, 180] loss: 1.077
[38, 240] loss: 1.080
[38, 300] loss: 1.099
[38, 360] loss: 1.079
Epoch: 38 -> Loss: 1.01064562798
Epoch: 38 -> Test Accuracy: 56.76
[39, 60] loss: 1.077
[39, 120] loss: 1.070
[39, 180] loss: 1.099
[39, 240] loss: 1.090
[39, 300] loss: 1.065
[39, 360] loss: 1.082
Epoch: 39 -> Loss: 0.90257537365
Epoch: 39 -> Test Accuracy: 56.6
[40, 60] loss: 1.070
[40, 120] loss: 1.087
[40, 180] loss: 1.071
[40, 240] loss: 1.080
[40, 300] loss: 1.086
[40, 360] loss: 1.083
Epoch: 40 -> Loss: 0.987020790577
Epoch: 40 -> Test Accuracy: 56.6
[41, 60] loss: 1.051
[41, 120] loss: 1.089
[41, 180] loss: 1.086
[41, 240] loss: 1.068
[41, 300] loss: 1.077
[41, 360] loss: 1.081
Epoch: 41 -> Loss: 1.08318543434
Epoch: 41 -> Test Accuracy: 57.24
[42, 60] loss: 1.083
[42, 120] loss: 1.054
[42, 180] loss: 1.072
[42, 240] loss: 1.069
[42, 300] loss: 1.075
[42, 360] loss: 1.086
Epoch: 42 -> Loss: 0.9835947752
Epoch: 42 -> Test Accuracy: 56.93
[43, 60] loss: 1.056
[43, 120] loss: 1.080
[43, 180] loss: 1.067
[43, 240] loss: 1.095
[43, 300] loss: 1.076
[43, 360] loss: 1.069
Epoch: 43 -> Loss: 1.08890414238
Epoch: 43 -> Test Accuracy: 57.08
[44, 60] loss: 1.078
[44, 120] loss: 1.065
[44, 180] loss: 1.072
[44, 240] loss: 1.089
[44, 300] loss: 1.072
[44, 360] loss: 1.075
Epoch: 44 -> Loss: 1.07605016232
Epoch: 44 -> Test Accuracy: 57.03
[45, 60] loss: 1.064
[45, 120] loss: 1.074
[45, 180] loss: 1.053
[45, 240] loss: 1.078
[45, 300] loss: 1.051
[45, 360] loss: 1.095
Epoch: 45 -> Loss: 1.16944479942
Epoch: 45 -> Test Accuracy: 57.08
[46, 60] loss: 1.057
[46, 120] loss: 1.080
[46, 180] loss: 1.089
[46, 240] loss: 1.065
[46, 300] loss: 1.076
[46, 360] loss: 1.075
Epoch: 46 -> Loss: 1.18470239639
Epoch: 46 -> Test Accuracy: 56.55
[47, 60] loss: 1.063
[47, 120] loss: 1.064
[47, 180] loss: 1.067
[47, 240] loss: 1.095
[47, 300] loss: 1.055
[47, 360] loss: 1.070
Epoch: 47 -> Loss: 1.24658799171
Epoch: 47 -> Test Accuracy: 57.31
[48, 60] loss: 1.065
[48, 120] loss: 1.077
[48, 180] loss: 1.075
[48, 240] loss: 1.097
[48, 300] loss: 1.085
[48, 360] loss: 1.042
Epoch: 48 -> Loss: 1.10153579712
Epoch: 48 -> Test Accuracy: 57.02
[49, 60] loss: 1.071
[49, 120] loss: 1.100
[49, 180] loss: 1.078
[49, 240] loss: 1.088
[49, 300] loss: 1.048
[49, 360] loss: 1.077
Epoch: 49 -> Loss: 1.06397080421
Epoch: 49 -> Test Accuracy: 55.96
[50, 60] loss: 1.067
[50, 120] loss: 1.063
[50, 180] loss: 1.069
[50, 240] loss: 1.073
[50, 300] loss: 1.082
[50, 360] loss: 1.074
Epoch: 50 -> Loss: 1.01605772972
Epoch: 50 -> Test Accuracy: 56.64
[51, 60] loss: 1.076
[51, 120] loss: 1.099
[51, 180] loss: 1.069
[51, 240] loss: 1.062
[51, 300] loss: 1.081
[51, 360] loss: 1.075
Epoch: 51 -> Loss: 0.962502360344
Epoch: 51 -> Test Accuracy: 56.47
[52, 60] loss: 1.090
[52, 120] loss: 1.079
[52, 180] loss: 1.045
[52, 240] loss: 1.064
[52, 300] loss: 1.083
[52, 360] loss: 1.047
Epoch: 52 -> Loss: 1.1834911108
Epoch: 52 -> Test Accuracy: 56.96
[53, 60] loss: 1.054
[53, 120] loss: 1.051
[53, 180] loss: 1.050
[53, 240] loss: 1.085
[53, 300] loss: 1.069
[53, 360] loss: 1.078
Epoch: 53 -> Loss: 1.04760217667
Epoch: 53 -> Test Accuracy: 57.01
[54, 60] loss: 1.051
[54, 120] loss: 1.065
[54, 180] loss: 1.095
[54, 240] loss: 1.070
[54, 300] loss: 1.064
[54, 360] loss: 1.057
Epoch: 54 -> Loss: 1.19820570946
Epoch: 54 -> Test Accuracy: 57.88
[55, 60] loss: 1.073
[55, 120] loss: 1.072
[55, 180] loss: 1.066
[55, 240] loss: 1.072
[55, 300] loss: 1.062
[55, 360] loss: 1.043
Epoch: 55 -> Loss: 1.05775618553
Epoch: 55 -> Test Accuracy: 56.67
[56, 60] loss: 1.057
[56, 120] loss: 1.084
[56, 180] loss: 1.067
[56, 240] loss: 1.079
[56, 300] loss: 1.062
[56, 360] loss: 1.083
Epoch: 56 -> Loss: 1.13094699383
Epoch: 56 -> Test Accuracy: 57.29
[57, 60] loss: 1.074
[57, 120] loss: 1.066
[57, 180] loss: 1.067
[57, 240] loss: 1.046
[57, 300] loss: 1.060
[57, 360] loss: 1.073
Epoch: 57 -> Loss: 1.11085772514
Epoch: 57 -> Test Accuracy: 56.35
[58, 60] loss: 1.057
[58, 120] loss: 1.077
[58, 180] loss: 1.072
[58, 240] loss: 1.059
[58, 300] loss: 1.078
[58, 360] loss: 1.051
Epoch: 58 -> Loss: 1.01605200768
Epoch: 58 -> Test Accuracy: 56.65
[59, 60] loss: 1.047
[59, 120] loss: 1.072
[59, 180] loss: 1.055
[59, 240] loss: 1.073
[59, 300] loss: 1.057
[59, 360] loss: 1.094
Epoch: 59 -> Loss: 1.15581321716
Epoch: 59 -> Test Accuracy: 57.27
[60, 60] loss: 1.054
[60, 120] loss: 1.061
[60, 180] loss: 1.060
[60, 240] loss: 1.056
[60, 300] loss: 1.073
[60, 360] loss: 1.062
Epoch: 60 -> Loss: 1.01332175732
Epoch: 60 -> Test Accuracy: 56.89
[61, 60] loss: 1.047
[61, 120] loss: 1.066
[61, 180] loss: 1.069
[61, 240] loss: 1.065
[61, 300] loss: 1.076
[61, 360] loss: 1.091
Epoch: 61 -> Loss: 0.988394081593
Epoch: 61 -> Test Accuracy: 56.44
[62, 60] loss: 1.083
[62, 120] loss: 1.070
[62, 180] loss: 1.048
[62, 240] loss: 1.078
[62, 300] loss: 1.059
[62, 360] loss: 1.068
Epoch: 62 -> Loss: 1.20921587944
Epoch: 62 -> Test Accuracy: 57.19
[63, 60] loss: 1.033
[63, 120] loss: 1.043
[63, 180] loss: 1.060
[63, 240] loss: 1.058
[63, 300] loss: 1.086
[63, 360] loss: 1.087
Epoch: 63 -> Loss: 1.16820049286
Epoch: 63 -> Test Accuracy: 57.68
[64, 60] loss: 1.067
[64, 120] loss: 1.063
[64, 180] loss: 1.053
[64, 240] loss: 1.084
[64, 300] loss: 1.055
[64, 360] loss: 1.076
Epoch: 64 -> Loss: 1.00641024113
Epoch: 64 -> Test Accuracy: 56.5
[65, 60] loss: 1.046
[65, 120] loss: 1.086
[65, 180] loss: 1.044
[65, 240] loss: 1.064
[65, 300] loss: 1.075
[65, 360] loss: 1.062
Epoch: 65 -> Loss: 1.07456374168
Epoch: 65 -> Test Accuracy: 57.14
[66, 60] loss: 1.066
[66, 120] loss: 1.067
[66, 180] loss: 1.052
[66, 240] loss: 1.064
[66, 300] loss: 1.057
[66, 360] loss: 1.052
Epoch: 66 -> Loss: 1.03426754475
Epoch: 66 -> Test Accuracy: 56.79
[67, 60] loss: 1.050
[67, 120] loss: 1.056
[67, 180] loss: 1.062
[67, 240] loss: 1.044
[67, 300] loss: 1.083
[67, 360] loss: 1.075
Epoch: 67 -> Loss: 1.13846027851
Epoch: 67 -> Test Accuracy: 57.7
[68, 60] loss: 1.076
[68, 120] loss: 1.053
[68, 180] loss: 1.060
[68, 240] loss: 1.075
[68, 300] loss: 1.052
[68, 360] loss: 1.055
Epoch: 68 -> Loss: 1.14730751514
Epoch: 68 -> Test Accuracy: 56.93
[69, 60] loss: 1.043
[69, 120] loss: 1.051
[69, 180] loss: 1.069
[69, 240] loss: 1.047
[69, 300] loss: 1.064
[69, 360] loss: 1.078
Epoch: 69 -> Loss: 1.09532833099
Epoch: 69 -> Test Accuracy: 57.55
[70, 60] loss: 1.074
[70, 120] loss: 1.060
[70, 180] loss: 1.069
[70, 240] loss: 1.065
[70, 300] loss: 1.055
[70, 360] loss: 1.057
Epoch: 70 -> Loss: 1.0877995491
Epoch: 70 -> Test Accuracy: 57.3
[71, 60] loss: 1.026
[71, 120] loss: 0.990
[71, 180] loss: 0.984
[71, 240] loss: 0.977
[71, 300] loss: 0.987
[71, 360] loss: 0.970
Epoch: 71 -> Loss: 1.03793370724
Epoch: 71 -> Test Accuracy: 60.14
[72, 60] loss: 0.962
[72, 120] loss: 0.961
[72, 180] loss: 0.965
[72, 240] loss: 0.972
[72, 300] loss: 0.973
[72, 360] loss: 0.975
Epoch: 72 -> Loss: 0.926759541035
Epoch: 72 -> Test Accuracy: 61.0
[73, 60] loss: 0.973
[73, 120] loss: 0.963
[73, 180] loss: 0.976
[73, 240] loss: 0.944
[73, 300] loss: 0.962
[73, 360] loss: 0.968
Epoch: 73 -> Loss: 1.03633105755
Epoch: 73 -> Test Accuracy: 60.63
[74, 60] loss: 0.957
[74, 120] loss: 0.953
[74, 180] loss: 0.968
[74, 240] loss: 0.938
[74, 300] loss: 0.959
[74, 360] loss: 0.964
Epoch: 74 -> Loss: 0.968690276146
Epoch: 74 -> Test Accuracy: 60.56
[75, 60] loss: 0.942
[75, 120] loss: 0.962
[75, 180] loss: 0.942
[75, 240] loss: 0.951
[75, 300] loss: 0.956
[75, 360] loss: 0.946
Epoch: 75 -> Loss: 0.793680846691
Epoch: 75 -> Test Accuracy: 60.38
[76, 60] loss: 0.930
[76, 120] loss: 0.953
[76, 180] loss: 0.942
[76, 240] loss: 0.952
[76, 300] loss: 0.960
[76, 360] loss: 0.956
Epoch: 76 -> Loss: 0.90732383728
Epoch: 76 -> Test Accuracy: 60.94
[77, 60] loss: 0.943
[77, 120] loss: 0.951
[77, 180] loss: 0.960
[77, 240] loss: 0.955
[77, 300] loss: 0.956
[77, 360] loss: 0.943
Epoch: 77 -> Loss: 0.98617619276
Epoch: 77 -> Test Accuracy: 61.15
[78, 60] loss: 0.948
[78, 120] loss: 0.938
[78, 180] loss: 0.961
[78, 240] loss: 0.957
[78, 300] loss: 0.959
[78, 360] loss: 0.946
Epoch: 78 -> Loss: 0.999989330769
Epoch: 78 -> Test Accuracy: 60.75
[79, 60] loss: 0.918
[79, 120] loss: 0.954
[79, 180] loss: 0.962
[79, 240] loss: 0.949
[79, 300] loss: 0.948
[79, 360] loss: 0.980
Epoch: 79 -> Loss: 0.876459002495
Epoch: 79 -> Test Accuracy: 60.38
[80, 60] loss: 0.943
[80, 120] loss: 0.929
[80, 180] loss: 0.947
[80, 240] loss: 0.979
[80, 300] loss: 0.953
[80, 360] loss: 0.958
Epoch: 80 -> Loss: 0.940453529358
Epoch: 80 -> Test Accuracy: 60.24
[81, 60] loss: 0.949
[81, 120] loss: 0.926
[81, 180] loss: 0.955
[81, 240] loss: 0.954
[81, 300] loss: 0.939
[81, 360] loss: 0.953
Epoch: 81 -> Loss: 1.0682592392
Epoch: 81 -> Test Accuracy: 60.5
[82, 60] loss: 0.945
[82, 120] loss: 0.936
[82, 180] loss: 0.953
[82, 240] loss: 0.941
[82, 300] loss: 0.965
[82, 360] loss: 0.955
Epoch: 82 -> Loss: 1.0175538063
Epoch: 82 -> Test Accuracy: 60.48
[83, 60] loss: 0.945
[83, 120] loss: 0.948
[83, 180] loss: 0.934
[83, 240] loss: 0.957
[83, 300] loss: 0.932
[83, 360] loss: 0.948
Epoch: 83 -> Loss: 0.960919976234
Epoch: 83 -> Test Accuracy: 60.91
[84, 60] loss: 0.947
[84, 120] loss: 0.954
[84, 180] loss: 0.942
[84, 240] loss: 0.930
[84, 300] loss: 0.935
[84, 360] loss: 0.949
Epoch: 84 -> Loss: 0.96836745739
Epoch: 84 -> Test Accuracy: 60.92
[85, 60] loss: 0.941
[85, 120] loss: 0.924
[85, 180] loss: 0.941
[85, 240] loss: 0.969
[85, 300] loss: 0.931
[85, 360] loss: 0.955
Epoch: 85 -> Loss: 0.838882446289
Epoch: 85 -> Test Accuracy: 60.38
[86, 60] loss: 0.918
[86, 120] loss: 0.921
[86, 180] loss: 0.934
[86, 240] loss: 0.923
[86, 300] loss: 0.895
[86, 360] loss: 0.918
Epoch: 86 -> Loss: 0.973382174969
Epoch: 86 -> Test Accuracy: 61.58
[87, 60] loss: 0.917
[87, 120] loss: 0.908
[87, 180] loss: 0.925
[87, 240] loss: 0.900
[87, 300] loss: 0.900
[87, 360] loss: 0.899
Epoch: 87 -> Loss: 0.78806501627
Epoch: 87 -> Test Accuracy: 61.5
[88, 60] loss: 0.900
[88, 120] loss: 0.884
[88, 180] loss: 0.924
[88, 240] loss: 0.902
[88, 300] loss: 0.883
[88, 360] loss: 0.911
Epoch: 88 -> Loss: 1.02360868454
Epoch: 88 -> Test Accuracy: 61.6
[89, 60] loss: 0.908
[89, 120] loss: 0.915
[89, 180] loss: 0.895
[89, 240] loss: 0.903
[89, 300] loss: 0.900
[89, 360] loss: 0.905
Epoch: 89 -> Loss: 0.874146580696
Epoch: 89 -> Test Accuracy: 61.86
[90, 60] loss: 0.884
[90, 120] loss: 0.894
[90, 180] loss: 0.901
[90, 240] loss: 0.902
[90, 300] loss: 0.903
[90, 360] loss: 0.893
Epoch: 90 -> Loss: 1.05563819408
Epoch: 90 -> Test Accuracy: 61.69
[91, 60] loss: 0.897
[91, 120] loss: 0.905
[91, 180] loss: 0.898
[91, 240] loss: 0.892
[91, 300] loss: 0.913
[91, 360] loss: 0.901
Epoch: 91 -> Loss: 0.829767525196
Epoch: 91 -> Test Accuracy: 61.99
[92, 60] loss: 0.894
[92, 120] loss: 0.921
[92, 180] loss: 0.899
[92, 240] loss: 0.905
[92, 300] loss: 0.905
[92, 360] loss: 0.893
Epoch: 92 -> Loss: 1.0015989542
Epoch: 92 -> Test Accuracy: 61.87
[93, 60] loss: 0.892
[93, 120] loss: 0.907
[93, 180] loss: 0.910
[93, 240] loss: 0.913
[93, 300] loss: 0.886
[93, 360] loss: 0.907
Epoch: 93 -> Loss: 0.93932056427
Epoch: 93 -> Test Accuracy: 62.14
[94, 60] loss: 0.889
[94, 120] loss: 0.920
[94, 180] loss: 0.905
[94, 240] loss: 0.899
[94, 300] loss: 0.880
[94, 360] loss: 0.887
Epoch: 94 -> Loss: 1.04583060741
Epoch: 94 -> Test Accuracy: 61.96
[95, 60] loss: 0.905
[95, 120] loss: 0.894
[95, 180] loss: 0.888
[95, 240] loss: 0.920
[95, 300] loss: 0.913
[95, 360] loss: 0.884
Epoch: 95 -> Loss: 0.791828036308
Epoch: 95 -> Test Accuracy: 61.88
[96, 60] loss: 0.888
[96, 120] loss: 0.893
[96, 180] loss: 0.903
[96, 240] loss: 0.905
[96, 300] loss: 0.891
[96, 360] loss: 0.889
Epoch: 96 -> Loss: 0.908858656883
Epoch: 96 -> Test Accuracy: 62.08
[97, 60] loss: 0.875
[97, 120] loss: 0.908
[97, 180] loss: 0.913
[97, 240] loss: 0.902
[97, 300] loss: 0.892
[97, 360] loss: 0.905
Epoch: 97 -> Loss: 0.757797002792
Epoch: 97 -> Test Accuracy: 61.86
[98, 60] loss: 0.886
[98, 120] loss: 0.902
[98, 180] loss: 0.905
[98, 240] loss: 0.897
[98, 300] loss: 0.903
[98, 360] loss: 0.882
Epoch: 98 -> Loss: 0.870647132397
Epoch: 98 -> Test Accuracy: 61.84
[99, 60] loss: 0.908
[99, 120] loss: 0.892
[99, 180] loss: 0.893
[99, 240] loss: 0.900
[99, 300] loss: 0.889
[99, 360] loss: 0.883
Epoch: 99 -> Loss: 0.933106899261
Epoch: 99 -> Test Accuracy: 62.03
[100, 60] loss: 0.890
[100, 120] loss: 0.895
[100, 180] loss: 0.881
[100, 240] loss: 0.893
[100, 300] loss: 0.891
[100, 360] loss: 0.896
Epoch: 100 -> Loss: 0.887984871864
Epoch: 100 -> Test Accuracy: 61.91
Finished Training
In [16]:
# save variables
fm.save_variable([rot_block3_loss_log, rot_block3_test_accuracy_log, 
                  block3_loss_log, block3_test_accuracy_log, 
                  conv_block3_loss_log, conv_block3_test_accuracy_log], "3_block_net")
In [17]:
# rename files
fm.add_block_to_name(3, [100, 200])

4 Block RotNet

In [6]:
# initialize network
net_block4 = RN.RotNet(num_classes=4, num_conv_block=4, add_avg_pool=False)
In [7]:
# train network
rot_block4_loss_log, _, rot_block4_test_accuracy_log, _, _ = tr.adaptive_learning([0.1, 0.02, 0.004, 0.0008], 
    [60, 120, 160, 200], 0.9, 5e-4, net_block4, criterion, trainloader, None, testloader, rot=['90', '180', '270'])
functionalities/rotation.py:16: UserWarning: torch.range is deprecated in favor of torch.arange and will be removed in 0.5. Note that arange generates values in [start; end), not [start; end].
  flip_idx = torch.range(trans_im.size(2) - 1, 0, -1).long()
functionalities/rotation.py:31: UserWarning: torch.range is deprecated in favor of torch.arange and will be removed in 0.5. Note that arange generates values in [start; end), not [start; end].
  vert_idx = torch.range(image.size(2) - 1, 0, -1).long()
functionalities/rotation.py:35: UserWarning: torch.range is deprecated in favor of torch.arange and will be removed in 0.5. Note that arange generates values in [start; end), not [start; end].
  hor_idx = torch.range(vert_im.size(1) - 1, 0, -1).long()
functionalities/rotation.py:50: UserWarning: torch.range is deprecated in favor of torch.arange and will be removed in 0.5. Note that arange generates values in [start; end), not [start; end].
  vert_idx = torch.range(image.size(2) - 1, 0, -1).long()
[1, 60] loss: 1.280
[1, 120] loss: 1.013
[1, 180] loss: 0.969
[1, 240] loss: 0.922
[1, 300] loss: 0.892
[1, 360] loss: 0.833
Epoch: 1 -> Loss: 0.88242161274
Epoch: 1 -> Test Accuracy: 65.2375
[2, 60] loss: 0.788
[2, 120] loss: 0.754
[2, 180] loss: 0.742
[2, 240] loss: 0.717
[2, 300] loss: 0.698
[2, 360] loss: 0.669
Epoch: 2 -> Loss: 0.673194825649
Epoch: 2 -> Test Accuracy: 73.2225
[3, 60] loss: 0.646
[3, 120] loss: 0.647
[3, 180] loss: 0.610
[3, 240] loss: 0.634
[3, 300] loss: 0.590
[3, 360] loss: 0.590
Epoch: 3 -> Loss: 0.540262639523
Epoch: 3 -> Test Accuracy: 75.73
[4, 60] loss: 0.553
[4, 120] loss: 0.572
[4, 180] loss: 0.568
[4, 240] loss: 0.564
[4, 300] loss: 0.545
[4, 360] loss: 0.531
Epoch: 4 -> Loss: 0.632760047913
Epoch: 4 -> Test Accuracy: 78.83
[5, 60] loss: 0.514
[5, 120] loss: 0.521
[5, 180] loss: 0.504
[5, 240] loss: 0.530
[5, 300] loss: 0.500
[5, 360] loss: 0.511
Epoch: 5 -> Loss: 0.40007519722
Epoch: 5 -> Test Accuracy: 80.075
[6, 60] loss: 0.487
[6, 120] loss: 0.484
[6, 180] loss: 0.494
[6, 240] loss: 0.486
[6, 300] loss: 0.482
[6, 360] loss: 0.475
Epoch: 6 -> Loss: 0.572343051434
Epoch: 6 -> Test Accuracy: 80.91
[7, 60] loss: 0.455
[7, 120] loss: 0.479
[7, 180] loss: 0.460
[7, 240] loss: 0.466
[7, 300] loss: 0.464
[7, 360] loss: 0.454
Epoch: 7 -> Loss: 0.433941841125
Epoch: 7 -> Test Accuracy: 82.3975
[8, 60] loss: 0.441
[8, 120] loss: 0.462
[8, 180] loss: 0.432
[8, 240] loss: 0.440
[8, 300] loss: 0.455
[8, 360] loss: 0.447
Epoch: 8 -> Loss: 0.374135434628
Epoch: 8 -> Test Accuracy: 82.39
[9, 60] loss: 0.426
[9, 120] loss: 0.440
[9, 180] loss: 0.437
[9, 240] loss: 0.436
[9, 300] loss: 0.422
[9, 360] loss: 0.416
Epoch: 9 -> Loss: 0.478541225195
Epoch: 9 -> Test Accuracy: 83.175
[10, 60] loss: 0.410
[10, 120] loss: 0.423
[10, 180] loss: 0.409
[10, 240] loss: 0.428
[10, 300] loss: 0.415
[10, 360] loss: 0.413
Epoch: 10 -> Loss: 0.467094898224
Epoch: 10 -> Test Accuracy: 82.305
[11, 60] loss: 0.405
[11, 120] loss: 0.406
[11, 180] loss: 0.421
[11, 240] loss: 0.403
[11, 300] loss: 0.405
[11, 360] loss: 0.401
Epoch: 11 -> Loss: 0.471219927073
Epoch: 11 -> Test Accuracy: 83.6775
[12, 60] loss: 0.387
[12, 120] loss: 0.400
[12, 180] loss: 0.406
[12, 240] loss: 0.388
[12, 300] loss: 0.382
[12, 360] loss: 0.408
Epoch: 12 -> Loss: 0.430007696152
Epoch: 12 -> Test Accuracy: 83.945
[13, 60] loss: 0.384
[13, 120] loss: 0.387
[13, 180] loss: 0.377
[13, 240] loss: 0.385
[13, 300] loss: 0.380
[13, 360] loss: 0.399
Epoch: 13 -> Loss: 0.338429063559
Epoch: 13 -> Test Accuracy: 84.635
[14, 60] loss: 0.369
[14, 120] loss: 0.384
[14, 180] loss: 0.371
[14, 240] loss: 0.386
[14, 300] loss: 0.378
[14, 360] loss: 0.392
Epoch: 14 -> Loss: 0.285325229168
Epoch: 14 -> Test Accuracy: 83.9125
[15, 60] loss: 0.370
[15, 120] loss: 0.370
[15, 180] loss: 0.380
[15, 240] loss: 0.363
[15, 300] loss: 0.378
[15, 360] loss: 0.376
Epoch: 15 -> Loss: 0.269873142242
Epoch: 15 -> Test Accuracy: 85.4075
[16, 60] loss: 0.351
[16, 120] loss: 0.369
[16, 180] loss: 0.366
[16, 240] loss: 0.367
[16, 300] loss: 0.377
[16, 360] loss: 0.374
Epoch: 16 -> Loss: 0.428826898336
Epoch: 16 -> Test Accuracy: 84.875
[17, 60] loss: 0.357
[17, 120] loss: 0.342
[17, 180] loss: 0.373
[17, 240] loss: 0.355
[17, 300] loss: 0.364
[17, 360] loss: 0.364
Epoch: 17 -> Loss: 0.411879599094
Epoch: 17 -> Test Accuracy: 85.5875
[18, 60] loss: 0.345
[18, 120] loss: 0.349
[18, 180] loss: 0.358
[18, 240] loss: 0.357
[18, 300] loss: 0.364
[18, 360] loss: 0.359
Epoch: 18 -> Loss: 0.339927792549
Epoch: 18 -> Test Accuracy: 85.1625
[19, 60] loss: 0.342
[19, 120] loss: 0.361
[19, 180] loss: 0.350
[19, 240] loss: 0.351
[19, 300] loss: 0.352
[19, 360] loss: 0.370
Epoch: 19 -> Loss: 0.245223641396
Epoch: 19 -> Test Accuracy: 85.005
[20, 60] loss: 0.341
[20, 120] loss: 0.336
[20, 180] loss: 0.338
[20, 240] loss: 0.366
[20, 300] loss: 0.356
[20, 360] loss: 0.352
Epoch: 20 -> Loss: 0.386262714863
Epoch: 20 -> Test Accuracy: 85.4325
[21, 60] loss: 0.341
[21, 120] loss: 0.335
[21, 180] loss: 0.358
[21, 240] loss: 0.350
[21, 300] loss: 0.347
[21, 360] loss: 0.347
Epoch: 21 -> Loss: 0.457043260336
Epoch: 21 -> Test Accuracy: 86.125
[22, 60] loss: 0.332
[22, 120] loss: 0.342
[22, 180] loss: 0.338
[22, 240] loss: 0.333
[22, 300] loss: 0.356
[22, 360] loss: 0.347
Epoch: 22 -> Loss: 0.478622019291
Epoch: 22 -> Test Accuracy: 86.1325
[23, 60] loss: 0.325
[23, 120] loss: 0.333
[23, 180] loss: 0.340
[23, 240] loss: 0.335
[23, 300] loss: 0.336
[23, 360] loss: 0.351
Epoch: 23 -> Loss: 0.37189117074
Epoch: 23 -> Test Accuracy: 86.0975
[24, 60] loss: 0.332
[24, 120] loss: 0.334
[24, 180] loss: 0.350
[24, 240] loss: 0.322
[24, 300] loss: 0.331
[24, 360] loss: 0.342
Epoch: 24 -> Loss: 0.315701901913
Epoch: 24 -> Test Accuracy: 84.5375
[25, 60] loss: 0.325
[25, 120] loss: 0.332
[25, 180] loss: 0.326
[25, 240] loss: 0.338
[25, 300] loss: 0.342
[25, 360] loss: 0.345
Epoch: 25 -> Loss: 0.295279443264
Epoch: 25 -> Test Accuracy: 86.34
[26, 60] loss: 0.334
[26, 120] loss: 0.326
[26, 180] loss: 0.325
[26, 240] loss: 0.348
[26, 300] loss: 0.331
[26, 360] loss: 0.332
Epoch: 26 -> Loss: 0.388910293579
Epoch: 26 -> Test Accuracy: 85.57
[27, 60] loss: 0.315
[27, 120] loss: 0.327
[27, 180] loss: 0.334
[27, 240] loss: 0.323
[27, 300] loss: 0.337
[27, 360] loss: 0.329
Epoch: 27 -> Loss: 0.313533097506
Epoch: 27 -> Test Accuracy: 85.2975
[28, 60] loss: 0.310
[28, 120] loss: 0.329
[28, 180] loss: 0.321
[28, 240] loss: 0.330
[28, 300] loss: 0.340
[28, 360] loss: 0.330
Epoch: 28 -> Loss: 0.30606174469
Epoch: 28 -> Test Accuracy: 86.3625
[29, 60] loss: 0.311
[29, 120] loss: 0.319
[29, 180] loss: 0.329
[29, 240] loss: 0.326
[29, 300] loss: 0.331
[29, 360] loss: 0.335
Epoch: 29 -> Loss: 0.292320221663
Epoch: 29 -> Test Accuracy: 86.36
[30, 60] loss: 0.304
[30, 120] loss: 0.323
[30, 180] loss: 0.326
[30, 240] loss: 0.323
[30, 300] loss: 0.311
[30, 360] loss: 0.326
Epoch: 30 -> Loss: 0.377558887005
Epoch: 30 -> Test Accuracy: 85.0525
[31, 60] loss: 0.323
[31, 120] loss: 0.319
[31, 180] loss: 0.314
[31, 240] loss: 0.307
[31, 300] loss: 0.331
[31, 360] loss: 0.324
Epoch: 31 -> Loss: 0.309758841991
Epoch: 31 -> Test Accuracy: 86.3675
[32, 60] loss: 0.301
[32, 120] loss: 0.313
[32, 180] loss: 0.326
[32, 240] loss: 0.313
[32, 300] loss: 0.326
[32, 360] loss: 0.328
Epoch: 32 -> Loss: 0.241686820984
Epoch: 32 -> Test Accuracy: 85.84
[33, 60] loss: 0.316
[33, 120] loss: 0.313
[33, 180] loss: 0.312
[33, 240] loss: 0.325
[33, 300] loss: 0.319
[33, 360] loss: 0.323
Epoch: 33 -> Loss: 0.333684593439
Epoch: 33 -> Test Accuracy: 86.545
[34, 60] loss: 0.312
[34, 120] loss: 0.312
[34, 180] loss: 0.322
[34, 240] loss: 0.321
[34, 300] loss: 0.322
[34, 360] loss: 0.311
Epoch: 34 -> Loss: 0.388200581074
Epoch: 34 -> Test Accuracy: 85.8125
[35, 60] loss: 0.312
[35, 120] loss: 0.307
[35, 180] loss: 0.321
[35, 240] loss: 0.313
[35, 300] loss: 0.323
[35, 360] loss: 0.323
Epoch: 35 -> Loss: 0.271653354168
Epoch: 35 -> Test Accuracy: 86.68
[36, 60] loss: 0.299
[36, 120] loss: 0.304
[36, 180] loss: 0.314
[36, 240] loss: 0.329
[36, 300] loss: 0.316
[36, 360] loss: 0.326
Epoch: 36 -> Loss: 0.332448899746
Epoch: 36 -> Test Accuracy: 85.805
[37, 60] loss: 0.300
[37, 120] loss: 0.307
[37, 180] loss: 0.316
[37, 240] loss: 0.315
[37, 300] loss: 0.318
[37, 360] loss: 0.312
Epoch: 37 -> Loss: 0.253511637449
Epoch: 37 -> Test Accuracy: 86.8125
[38, 60] loss: 0.302
[38, 120] loss: 0.314
[38, 180] loss: 0.312
[38, 240] loss: 0.315
[38, 300] loss: 0.313
[38, 360] loss: 0.309
Epoch: 38 -> Loss: 0.288749873638
Epoch: 38 -> Test Accuracy: 86.67
[39, 60] loss: 0.292
[39, 120] loss: 0.321
[39, 180] loss: 0.322
[39, 240] loss: 0.305
[39, 300] loss: 0.308
[39, 360] loss: 0.330
Epoch: 39 -> Loss: 0.351736664772
Epoch: 39 -> Test Accuracy: 86.85
[40, 60] loss: 0.297
[40, 120] loss: 0.315
[40, 180] loss: 0.308
[40, 240] loss: 0.321
[40, 300] loss: 0.308
[40, 360] loss: 0.323
Epoch: 40 -> Loss: 0.299942553043
Epoch: 40 -> Test Accuracy: 86.61
[41, 60] loss: 0.301
[41, 120] loss: 0.305
[41, 180] loss: 0.295
[41, 240] loss: 0.319
[41, 300] loss: 0.305
[41, 360] loss: 0.316
Epoch: 41 -> Loss: 0.281109631062
Epoch: 41 -> Test Accuracy: 87.31
[42, 60] loss: 0.290
[42, 120] loss: 0.320
[42, 180] loss: 0.308
[42, 240] loss: 0.302
[42, 300] loss: 0.311
[42, 360] loss: 0.311
Epoch: 42 -> Loss: 0.37639850378
Epoch: 42 -> Test Accuracy: 86.91
[43, 60] loss: 0.300
[43, 120] loss: 0.310
[43, 180] loss: 0.313
[43, 240] loss: 0.310
[43, 300] loss: 0.307
[43, 360] loss: 0.306
Epoch: 43 -> Loss: 0.322246402502
Epoch: 43 -> Test Accuracy: 85.7625
[44, 60] loss: 0.300
[44, 120] loss: 0.294
[44, 180] loss: 0.306
[44, 240] loss: 0.317
[44, 300] loss: 0.324
[44, 360] loss: 0.302
Epoch: 44 -> Loss: 0.329386383295
Epoch: 44 -> Test Accuracy: 86.535
[45, 60] loss: 0.285
[45, 120] loss: 0.302
[45, 180] loss: 0.310
[45, 240] loss: 0.303
[45, 300] loss: 0.316
[45, 360] loss: 0.320
Epoch: 45 -> Loss: 0.343748152256
Epoch: 45 -> Test Accuracy: 86.4875
[46, 60] loss: 0.297
[46, 120] loss: 0.300
[46, 180] loss: 0.307
[46, 240] loss: 0.305
[46, 300] loss: 0.321
[46, 360] loss: 0.317
Epoch: 46 -> Loss: 0.241001099348
Epoch: 46 -> Test Accuracy: 87.6325
[47, 60] loss: 0.287
[47, 120] loss: 0.305
[47, 180] loss: 0.306
[47, 240] loss: 0.311
[47, 300] loss: 0.309
[47, 360] loss: 0.313
Epoch: 47 -> Loss: 0.264798223972
Epoch: 47 -> Test Accuracy: 86.7775
[48, 60] loss: 0.292
[48, 120] loss: 0.300
[48, 180] loss: 0.297
[48, 240] loss: 0.308
[48, 300] loss: 0.299
[48, 360] loss: 0.315
Epoch: 48 -> Loss: 0.321404635906
Epoch: 48 -> Test Accuracy: 86.86
[49, 60] loss: 0.291
[49, 120] loss: 0.304
[49, 180] loss: 0.294
[49, 240] loss: 0.318
[49, 300] loss: 0.318
[49, 360] loss: 0.298
Epoch: 49 -> Loss: 0.368998676538
Epoch: 49 -> Test Accuracy: 87.6475
[50, 60] loss: 0.304
[50, 120] loss: 0.302
[50, 180] loss: 0.299
[50, 240] loss: 0.311
[50, 300] loss: 0.300
[50, 360] loss: 0.313
Epoch: 50 -> Loss: 0.368564456701
Epoch: 50 -> Test Accuracy: 86.975
[51, 60] loss: 0.289
[51, 120] loss: 0.308
[51, 180] loss: 0.282
[51, 240] loss: 0.312
[51, 300] loss: 0.299
[51, 360] loss: 0.306
Epoch: 51 -> Loss: 0.376721441746
Epoch: 51 -> Test Accuracy: 85.77
[52, 60] loss: 0.283
[52, 120] loss: 0.298
[52, 180] loss: 0.300
[52, 240] loss: 0.305
[52, 300] loss: 0.298
[52, 360] loss: 0.300
Epoch: 52 -> Loss: 0.283972710371
Epoch: 52 -> Test Accuracy: 86.7825
[53, 60] loss: 0.286
[53, 120] loss: 0.298
[53, 180] loss: 0.291
[53, 240] loss: 0.299
[53, 300] loss: 0.301
[53, 360] loss: 0.304
Epoch: 53 -> Loss: 0.31412217021
Epoch: 53 -> Test Accuracy: 86.3825
[54, 60] loss: 0.298
[54, 120] loss: 0.286
[54, 180] loss: 0.312
[54, 240] loss: 0.295
[54, 300] loss: 0.292
[54, 360] loss: 0.301
Epoch: 54 -> Loss: 0.356736570597
Epoch: 54 -> Test Accuracy: 86.355
[55, 60] loss: 0.290
[55, 120] loss: 0.295
[55, 180] loss: 0.308
[55, 240] loss: 0.304
[55, 300] loss: 0.303
[55, 360] loss: 0.301
Epoch: 55 -> Loss: 0.331777065992
Epoch: 55 -> Test Accuracy: 86.665
[56, 60] loss: 0.289
[56, 120] loss: 0.297
[56, 180] loss: 0.299
[56, 240] loss: 0.307
[56, 300] loss: 0.287
[56, 360] loss: 0.321
Epoch: 56 -> Loss: 0.261130779982
Epoch: 56 -> Test Accuracy: 86.6275
[57, 60] loss: 0.280
[57, 120] loss: 0.289
[57, 180] loss: 0.309
[57, 240] loss: 0.309
[57, 300] loss: 0.301
[57, 360] loss: 0.302
Epoch: 57 -> Loss: 0.289793878794
Epoch: 57 -> Test Accuracy: 86.805
[58, 60] loss: 0.292
[58, 120] loss: 0.300
[58, 180] loss: 0.300
[58, 240] loss: 0.301
[58, 300] loss: 0.294
[58, 360] loss: 0.308
Epoch: 58 -> Loss: 0.391004383564
Epoch: 58 -> Test Accuracy: 86.545
[59, 60] loss: 0.299
[59, 120] loss: 0.292
[59, 180] loss: 0.300
[59, 240] loss: 0.288
[59, 300] loss: 0.304
[59, 360] loss: 0.296
Epoch: 59 -> Loss: 0.291617512703
Epoch: 59 -> Test Accuracy: 86.95
[60, 60] loss: 0.282
[60, 120] loss: 0.299
[60, 180] loss: 0.304
[60, 240] loss: 0.310
[60, 300] loss: 0.299
[60, 360] loss: 0.298
Epoch: 60 -> Loss: 0.328978180885
Epoch: 60 -> Test Accuracy: 87.535
[61, 60] loss: 0.225
[61, 120] loss: 0.194
[61, 180] loss: 0.181
[61, 240] loss: 0.183
[61, 300] loss: 0.183
[61, 360] loss: 0.183
Epoch: 61 -> Loss: 0.126734361053
Epoch: 61 -> Test Accuracy: 91.32
[62, 60] loss: 0.158
[62, 120] loss: 0.152
[62, 180] loss: 0.155
[62, 240] loss: 0.166
[62, 300] loss: 0.176
[62, 360] loss: 0.172
Epoch: 62 -> Loss: 0.174959421158
Epoch: 62 -> Test Accuracy: 91.5325
[63, 60] loss: 0.149
[63, 120] loss: 0.152
[63, 180] loss: 0.158
[63, 240] loss: 0.160
[63, 300] loss: 0.161
[63, 360] loss: 0.156
Epoch: 63 -> Loss: 0.209470033646
Epoch: 63 -> Test Accuracy: 91.5125
[64, 60] loss: 0.138
[64, 120] loss: 0.148
[64, 180] loss: 0.152
[64, 240] loss: 0.152
[64, 300] loss: 0.164
[64, 360] loss: 0.157
Epoch: 64 -> Loss: 0.136709198356
Epoch: 64 -> Test Accuracy: 91.1275
[65, 60] loss: 0.133
[65, 120] loss: 0.142
[65, 180] loss: 0.155
[65, 240] loss: 0.146
[65, 300] loss: 0.152
[65, 360] loss: 0.152
Epoch: 65 -> Loss: 0.118245780468
Epoch: 65 -> Test Accuracy: 91.0175
[66, 60] loss: 0.133
[66, 120] loss: 0.132
[66, 180] loss: 0.145
[66, 240] loss: 0.138
[66, 300] loss: 0.152
[66, 360] loss: 0.150
Epoch: 66 -> Loss: 0.106802843511
Epoch: 66 -> Test Accuracy: 90.7525
[67, 60] loss: 0.141
[67, 120] loss: 0.142
[67, 180] loss: 0.141
[67, 240] loss: 0.146
[67, 300] loss: 0.152
[67, 360] loss: 0.142
Epoch: 67 -> Loss: 0.183747336268
Epoch: 67 -> Test Accuracy: 90.9525
[68, 60] loss: 0.137
[68, 120] loss: 0.135
[68, 180] loss: 0.155
[68, 240] loss: 0.142
[68, 300] loss: 0.155
[68, 360] loss: 0.144
Epoch: 68 -> Loss: 0.127017647028
Epoch: 68 -> Test Accuracy: 90.4275
[69, 60] loss: 0.133
[69, 120] loss: 0.134
[69, 180] loss: 0.136
[69, 240] loss: 0.153
[69, 300] loss: 0.151
[69, 360] loss: 0.152
Epoch: 69 -> Loss: 0.146195858717
Epoch: 69 -> Test Accuracy: 90.5875
[70, 60] loss: 0.141
[70, 120] loss: 0.136
[70, 180] loss: 0.140
[70, 240] loss: 0.147
[70, 300] loss: 0.152
[70, 360] loss: 0.145
Epoch: 70 -> Loss: 0.144819706678
Epoch: 70 -> Test Accuracy: 90.445
[71, 60] loss: 0.133
[71, 120] loss: 0.143
[71, 180] loss: 0.135
[71, 240] loss: 0.152
[71, 300] loss: 0.149
[71, 360] loss: 0.158
Epoch: 71 -> Loss: 0.122240677476
Epoch: 71 -> Test Accuracy: 90.6475
[72, 60] loss: 0.133
[72, 120] loss: 0.139
[72, 180] loss: 0.146
[72, 240] loss: 0.144
[72, 300] loss: 0.149
[72, 360] loss: 0.152
Epoch: 72 -> Loss: 0.121262550354
Epoch: 72 -> Test Accuracy: 90.77
[73, 60] loss: 0.131
[73, 120] loss: 0.142
[73, 180] loss: 0.141
[73, 240] loss: 0.152
[73, 300] loss: 0.151
[73, 360] loss: 0.162
Epoch: 73 -> Loss: 0.174145117402
Epoch: 73 -> Test Accuracy: 90.6425
[74, 60] loss: 0.134
[74, 120] loss: 0.138
[74, 180] loss: 0.157
[74, 240] loss: 0.148
[74, 300] loss: 0.141
[74, 360] loss: 0.154
Epoch: 74 -> Loss: 0.0880781561136
Epoch: 74 -> Test Accuracy: 91.04
[75, 60] loss: 0.138
[75, 120] loss: 0.141
[75, 180] loss: 0.149
[75, 240] loss: 0.146
[75, 300] loss: 0.147
[75, 360] loss: 0.142
Epoch: 75 -> Loss: 0.126393944025
Epoch: 75 -> Test Accuracy: 90.82
[76, 60] loss: 0.134
[76, 120] loss: 0.136
[76, 180] loss: 0.136
[76, 240] loss: 0.157
[76, 300] loss: 0.148
[76, 360] loss: 0.150
Epoch: 76 -> Loss: 0.113145872951
Epoch: 76 -> Test Accuracy: 90.26
[77, 60] loss: 0.137
[77, 120] loss: 0.144
[77, 180] loss: 0.143
[77, 240] loss: 0.144
[77, 300] loss: 0.157
[77, 360] loss: 0.154
Epoch: 77 -> Loss: 0.189672544599
Epoch: 77 -> Test Accuracy: 90.6525
[78, 60] loss: 0.132
[78, 120] loss: 0.145
[78, 180] loss: 0.145
[78, 240] loss: 0.145
[78, 300] loss: 0.154
[78, 360] loss: 0.157
Epoch: 78 -> Loss: 0.134117081761
Epoch: 78 -> Test Accuracy: 90.925
[79, 60] loss: 0.136
[79, 120] loss: 0.133
[79, 180] loss: 0.146
[79, 240] loss: 0.144
[79, 300] loss: 0.153
[79, 360] loss: 0.155
Epoch: 79 -> Loss: 0.178136751056
Epoch: 79 -> Test Accuracy: 90.68
[80, 60] loss: 0.128
[80, 120] loss: 0.138
[80, 180] loss: 0.143
[80, 240] loss: 0.147
[80, 300] loss: 0.151
[80, 360] loss: 0.141
Epoch: 80 -> Loss: 0.193272918463
Epoch: 80 -> Test Accuracy: 90.4525
[81, 60] loss: 0.135
[81, 120] loss: 0.132
[81, 180] loss: 0.142
[81, 240] loss: 0.151
[81, 300] loss: 0.156
[81, 360] loss: 0.160
Epoch: 81 -> Loss: 0.140550598502
Epoch: 81 -> Test Accuracy: 90.345
[82, 60] loss: 0.136
[82, 120] loss: 0.140
[82, 180] loss: 0.139
[82, 240] loss: 0.152
[82, 300] loss: 0.152
[82, 360] loss: 0.154
Epoch: 82 -> Loss: 0.1007431373
Epoch: 82 -> Test Accuracy: 90.8
[83, 60] loss: 0.130
[83, 120] loss: 0.140
[83, 180] loss: 0.145
[83, 240] loss: 0.151
[83, 300] loss: 0.153
[83, 360] loss: 0.147
Epoch: 83 -> Loss: 0.162469476461
Epoch: 83 -> Test Accuracy: 90.6975
[84, 60] loss: 0.136
[84, 120] loss: 0.144
[84, 180] loss: 0.149
[84, 240] loss: 0.134
[84, 300] loss: 0.143
[84, 360] loss: 0.154
Epoch: 84 -> Loss: 0.161104053259
Epoch: 84 -> Test Accuracy: 90.5925
[85, 60] loss: 0.136
[85, 120] loss: 0.138
[85, 180] loss: 0.153
[85, 240] loss: 0.150
[85, 300] loss: 0.145
[85, 360] loss: 0.144
Epoch: 85 -> Loss: 0.124722383916
Epoch: 85 -> Test Accuracy: 90.1225
[86, 60] loss: 0.130
[86, 120] loss: 0.137
[86, 180] loss: 0.140
[86, 240] loss: 0.153
[86, 300] loss: 0.148
[86, 360] loss: 0.153
Epoch: 86 -> Loss: 0.141249030828
Epoch: 86 -> Test Accuracy: 90.0975
[87, 60] loss: 0.129
[87, 120] loss: 0.140
[87, 180] loss: 0.144
[87, 240] loss: 0.142
[87, 300] loss: 0.146
[87, 360] loss: 0.150
Epoch: 87 -> Loss: 0.164696201682
Epoch: 87 -> Test Accuracy: 90.755
[88, 60] loss: 0.133
[88, 120] loss: 0.135
[88, 180] loss: 0.150
[88, 240] loss: 0.139
[88, 300] loss: 0.149
[88, 360] loss: 0.156
Epoch: 88 -> Loss: 0.12807802856
Epoch: 88 -> Test Accuracy: 90.385
[89, 60] loss: 0.138
[89, 120] loss: 0.138
[89, 180] loss: 0.135
[89, 240] loss: 0.146
[89, 300] loss: 0.142
[89, 360] loss: 0.143
Epoch: 89 -> Loss: 0.131286710501
Epoch: 89 -> Test Accuracy: 90.56
[90, 60] loss: 0.129
[90, 120] loss: 0.134
[90, 180] loss: 0.138
[90, 240] loss: 0.146
[90, 300] loss: 0.151
[90, 360] loss: 0.154
Epoch: 90 -> Loss: 0.173849195242
Epoch: 90 -> Test Accuracy: 90.23
[91, 60] loss: 0.129
[91, 120] loss: 0.132
[91, 180] loss: 0.149
[91, 240] loss: 0.139
[91, 300] loss: 0.145
[91, 360] loss: 0.152
Epoch: 91 -> Loss: 0.149797052145
Epoch: 91 -> Test Accuracy: 89.45
[92, 60] loss: 0.128
[92, 120] loss: 0.137
[92, 180] loss: 0.137
[92, 240] loss: 0.154
[92, 300] loss: 0.147
[92, 360] loss: 0.141
Epoch: 92 -> Loss: 0.115692041814
Epoch: 92 -> Test Accuracy: 90.2425
[93, 60] loss: 0.124
[93, 120] loss: 0.134
[93, 180] loss: 0.131
[93, 240] loss: 0.140
[93, 300] loss: 0.145
[93, 360] loss: 0.153
Epoch: 93 -> Loss: 0.165478780866
Epoch: 93 -> Test Accuracy: 90.62
[94, 60] loss: 0.141
[94, 120] loss: 0.127
[94, 180] loss: 0.134
[94, 240] loss: 0.133
[94, 300] loss: 0.151
[94, 360] loss: 0.148
Epoch: 94 -> Loss: 0.180439129472
Epoch: 94 -> Test Accuracy: 90.4225
[95, 60] loss: 0.128
[95, 120] loss: 0.132
[95, 180] loss: 0.141
[95, 240] loss: 0.137
[95, 300] loss: 0.139
[95, 360] loss: 0.147
Epoch: 95 -> Loss: 0.18649956584
Epoch: 95 -> Test Accuracy: 90.5
[96, 60] loss: 0.132
[96, 120] loss: 0.132
[96, 180] loss: 0.134
[96, 240] loss: 0.138
[96, 300] loss: 0.148
[96, 360] loss: 0.137
Epoch: 96 -> Loss: 0.127098694444
Epoch: 96 -> Test Accuracy: 90.81
[97, 60] loss: 0.127
[97, 120] loss: 0.132
[97, 180] loss: 0.133
[97, 240] loss: 0.150
[97, 300] loss: 0.141
[97, 360] loss: 0.147
Epoch: 97 -> Loss: 0.130502581596
Epoch: 97 -> Test Accuracy: 90.665
[98, 60] loss: 0.131
[98, 120] loss: 0.132
[98, 180] loss: 0.135
[98, 240] loss: 0.144
[98, 300] loss: 0.138
[98, 360] loss: 0.140
Epoch: 98 -> Loss: 0.194058328867
Epoch: 98 -> Test Accuracy: 90.675
[99, 60] loss: 0.126
[99, 120] loss: 0.124
[99, 180] loss: 0.145
[99, 240] loss: 0.133
[99, 300] loss: 0.141
[99, 360] loss: 0.141
Epoch: 99 -> Loss: 0.0897030010819
Epoch: 99 -> Test Accuracy: 90.155
[100, 60] loss: 0.133
[100, 120] loss: 0.128
[100, 180] loss: 0.133
[100, 240] loss: 0.139
[100, 300] loss: 0.140
[100, 360] loss: 0.142
Epoch: 100 -> Loss: 0.149182528257
Epoch: 100 -> Test Accuracy: 90.305
[101, 60] loss: 0.124
[101, 120] loss: 0.135
[101, 180] loss: 0.132
[101, 240] loss: 0.138
[101, 300] loss: 0.143
[101, 360] loss: 0.134
Epoch: 101 -> Loss: 0.196269705892
Epoch: 101 -> Test Accuracy: 90.4925
[102, 60] loss: 0.123
[102, 120] loss: 0.130
[102, 180] loss: 0.134
[102, 240] loss: 0.142
[102, 300] loss: 0.140
[102, 360] loss: 0.139
Epoch: 102 -> Loss: 0.164475351572
Epoch: 102 -> Test Accuracy: 90.125
[103, 60] loss: 0.131
[103, 120] loss: 0.132
[103, 180] loss: 0.137
[103, 240] loss: 0.129
[103, 300] loss: 0.143
[103, 360] loss: 0.138
Epoch: 103 -> Loss: 0.356296956539
Epoch: 103 -> Test Accuracy: 90.2525
[104, 60] loss: 0.120
[104, 120] loss: 0.133
[104, 180] loss: 0.131
[104, 240] loss: 0.134
[104, 300] loss: 0.143
[104, 360] loss: 0.137
Epoch: 104 -> Loss: 0.180964976549
Epoch: 104 -> Test Accuracy: 90.5475
[105, 60] loss: 0.125
[105, 120] loss: 0.131
[105, 180] loss: 0.138
[105, 240] loss: 0.133
[105, 300] loss: 0.126
[105, 360] loss: 0.139
Epoch: 105 -> Loss: 0.1654535532
Epoch: 105 -> Test Accuracy: 90.33
[106, 60] loss: 0.127
[106, 120] loss: 0.129
[106, 180] loss: 0.133
[106, 240] loss: 0.138
[106, 300] loss: 0.137
[106, 360] loss: 0.150
Epoch: 106 -> Loss: 0.110846780241
Epoch: 106 -> Test Accuracy: 90.7225
[107, 60] loss: 0.131
[107, 120] loss: 0.135
[107, 180] loss: 0.134
[107, 240] loss: 0.136
[107, 300] loss: 0.133
[107, 360] loss: 0.146
Epoch: 107 -> Loss: 0.0828087627888
Epoch: 107 -> Test Accuracy: 90.3075
[108, 60] loss: 0.124
[108, 120] loss: 0.122
[108, 180] loss: 0.132
[108, 240] loss: 0.130
[108, 300] loss: 0.139
[108, 360] loss: 0.139
Epoch: 108 -> Loss: 0.22273555398
Epoch: 108 -> Test Accuracy: 90.74
[109, 60] loss: 0.128
[109, 120] loss: 0.128
[109, 180] loss: 0.130
[109, 240] loss: 0.144
[109, 300] loss: 0.140
[109, 360] loss: 0.139
Epoch: 109 -> Loss: 0.138728111982
Epoch: 109 -> Test Accuracy: 90.34
[110, 60] loss: 0.115
[110, 120] loss: 0.134
[110, 180] loss: 0.127
[110, 240] loss: 0.129
[110, 300] loss: 0.138
[110, 360] loss: 0.138
Epoch: 110 -> Loss: 0.154812350869
Epoch: 110 -> Test Accuracy: 90.495
[111, 60] loss: 0.124
[111, 120] loss: 0.127
[111, 180] loss: 0.130
[111, 240] loss: 0.136
[111, 300] loss: 0.133
[111, 360] loss: 0.135
Epoch: 111 -> Loss: 0.180580988526
Epoch: 111 -> Test Accuracy: 89.7225
[112, 60] loss: 0.136
[112, 120] loss: 0.125
[112, 180] loss: 0.132
[112, 240] loss: 0.130
[112, 300] loss: 0.137
[112, 360] loss: 0.140
Epoch: 112 -> Loss: 0.158955469728
Epoch: 112 -> Test Accuracy: 90.2075
[113, 60] loss: 0.127
[113, 120] loss: 0.122
[113, 180] loss: 0.133
[113, 240] loss: 0.138
[113, 300] loss: 0.134
[113, 360] loss: 0.139
Epoch: 113 -> Loss: 0.124354101717
Epoch: 113 -> Test Accuracy: 90.2075
[114, 60] loss: 0.124
[114, 120] loss: 0.121
[114, 180] loss: 0.133
[114, 240] loss: 0.126
[114, 300] loss: 0.137
[114, 360] loss: 0.140
Epoch: 114 -> Loss: 0.117385149002
Epoch: 114 -> Test Accuracy: 90.535
[115, 60] loss: 0.125
[115, 120] loss: 0.132
[115, 180] loss: 0.135
[115, 240] loss: 0.131
[115, 300] loss: 0.138
[115, 360] loss: 0.134
Epoch: 115 -> Loss: 0.206795409322
Epoch: 115 -> Test Accuracy: 90.715
[116, 60] loss: 0.112
[116, 120] loss: 0.129
[116, 180] loss: 0.134
[116, 240] loss: 0.130
[116, 300] loss: 0.139
[116, 360] loss: 0.135
Epoch: 116 -> Loss: 0.198866948485
Epoch: 116 -> Test Accuracy: 90.905
[117, 60] loss: 0.129
[117, 120] loss: 0.125
[117, 180] loss: 0.125
[117, 240] loss: 0.132
[117, 300] loss: 0.134
[117, 360] loss: 0.137
Epoch: 117 -> Loss: 0.152974322438
Epoch: 117 -> Test Accuracy: 90.8375
[118, 60] loss: 0.116
[118, 120] loss: 0.120
[118, 180] loss: 0.133
[118, 240] loss: 0.130
[118, 300] loss: 0.136
[118, 360] loss: 0.141
Epoch: 118 -> Loss: 0.0918317735195
Epoch: 118 -> Test Accuracy: 90.26
[119, 60] loss: 0.115
[119, 120] loss: 0.131
[119, 180] loss: 0.135
[119, 240] loss: 0.137
[119, 300] loss: 0.126
[119, 360] loss: 0.132
Epoch: 119 -> Loss: 0.214848846197
Epoch: 119 -> Test Accuracy: 90.455
[120, 60] loss: 0.124
[120, 120] loss: 0.127
[120, 180] loss: 0.131
[120, 240] loss: 0.135
[120, 300] loss: 0.136
[120, 360] loss: 0.139
Epoch: 120 -> Loss: 0.160332351923
Epoch: 120 -> Test Accuracy: 90.89
[121, 60] loss: 0.093
[121, 120] loss: 0.075
[121, 180] loss: 0.072
[121, 240] loss: 0.070
[121, 300] loss: 0.065
[121, 360] loss: 0.063
Epoch: 121 -> Loss: 0.0423624292016
Epoch: 121 -> Test Accuracy: 92.44
[122, 60] loss: 0.057
[122, 120] loss: 0.053
[122, 180] loss: 0.057
[122, 240] loss: 0.057
[122, 300] loss: 0.055
[122, 360] loss: 0.052
Epoch: 122 -> Loss: 0.0960680544376
Epoch: 122 -> Test Accuracy: 92.2625
[123, 60] loss: 0.046
[123, 120] loss: 0.046
[123, 180] loss: 0.050
[123, 240] loss: 0.050
[123, 300] loss: 0.048
[123, 360] loss: 0.048
Epoch: 123 -> Loss: 0.0571182966232
Epoch: 123 -> Test Accuracy: 92.36
[124, 60] loss: 0.043
[124, 120] loss: 0.044
[124, 180] loss: 0.043
[124, 240] loss: 0.045
[124, 300] loss: 0.045
[124, 360] loss: 0.044
Epoch: 124 -> Loss: 0.0507776737213
Epoch: 124 -> Test Accuracy: 92.2725
[125, 60] loss: 0.039
[125, 120] loss: 0.039
[125, 180] loss: 0.039
[125, 240] loss: 0.042
[125, 300] loss: 0.043
[125, 360] loss: 0.042
Epoch: 125 -> Loss: 0.0358629673719
Epoch: 125 -> Test Accuracy: 92.3075
[126, 60] loss: 0.042
[126, 120] loss: 0.038
[126, 180] loss: 0.039
[126, 240] loss: 0.040
[126, 300] loss: 0.038
[126, 360] loss: 0.035
Epoch: 126 -> Loss: 0.0723502635956
Epoch: 126 -> Test Accuracy: 92.44
[127, 60] loss: 0.033
[127, 120] loss: 0.032
[127, 180] loss: 0.036
[127, 240] loss: 0.036
[127, 300] loss: 0.038
[127, 360] loss: 0.040
Epoch: 127 -> Loss: 0.0361194983125
Epoch: 127 -> Test Accuracy: 92.2975
[128, 60] loss: 0.032
[128, 120] loss: 0.032
[128, 180] loss: 0.033
[128, 240] loss: 0.036
[128, 300] loss: 0.034
[128, 360] loss: 0.037
Epoch: 128 -> Loss: 0.0498210191727
Epoch: 128 -> Test Accuracy: 92.455
[129, 60] loss: 0.031
[129, 120] loss: 0.035
[129, 180] loss: 0.030
[129, 240] loss: 0.033
[129, 300] loss: 0.030
[129, 360] loss: 0.034
Epoch: 129 -> Loss: 0.0112496130168
Epoch: 129 -> Test Accuracy: 92.3725
[130, 60] loss: 0.029
[130, 120] loss: 0.032
[130, 180] loss: 0.033
[130, 240] loss: 0.030
[130, 300] loss: 0.033
[130, 360] loss: 0.031
Epoch: 130 -> Loss: 0.0396613068879
Epoch: 130 -> Test Accuracy: 92.2825
[131, 60] loss: 0.029
[131, 120] loss: 0.030
[131, 180] loss: 0.029
[131, 240] loss: 0.032
[131, 300] loss: 0.029
[131, 360] loss: 0.031
Epoch: 131 -> Loss: 0.0441687554121
Epoch: 131 -> Test Accuracy: 92.3325
[132, 60] loss: 0.029
[132, 120] loss: 0.028
[132, 180] loss: 0.029
[132, 240] loss: 0.030
[132, 300] loss: 0.033
[132, 360] loss: 0.030
Epoch: 132 -> Loss: 0.0172540359199
Epoch: 132 -> Test Accuracy: 92.155
[133, 60] loss: 0.028
[133, 120] loss: 0.029
[133, 180] loss: 0.030
[133, 240] loss: 0.031
[133, 300] loss: 0.031
[133, 360] loss: 0.028
Epoch: 133 -> Loss: 0.0438217520714
Epoch: 133 -> Test Accuracy: 92.26
[134, 60] loss: 0.027
[134, 120] loss: 0.027
[134, 180] loss: 0.028
[134, 240] loss: 0.027
[134, 300] loss: 0.030
[134, 360] loss: 0.030
Epoch: 134 -> Loss: 0.0326785072684
Epoch: 134 -> Test Accuracy: 92.4925
[135, 60] loss: 0.027
[135, 120] loss: 0.027
[135, 180] loss: 0.028
[135, 240] loss: 0.029
[135, 300] loss: 0.029
[135, 360] loss: 0.029
Epoch: 135 -> Loss: 0.0124627845362
Epoch: 135 -> Test Accuracy: 92.2775
[136, 60] loss: 0.023
[136, 120] loss: 0.027
[136, 180] loss: 0.026
[136, 240] loss: 0.027
[136, 300] loss: 0.029
[136, 360] loss: 0.031
Epoch: 136 -> Loss: 0.0220747478306
Epoch: 136 -> Test Accuracy: 92.11
[137, 60] loss: 0.022
[137, 120] loss: 0.025
[137, 180] loss: 0.028
[137, 240] loss: 0.024
[137, 300] loss: 0.029
[137, 360] loss: 0.028
Epoch: 137 -> Loss: 0.0135794626549
Epoch: 137 -> Test Accuracy: 92.16
[138, 60] loss: 0.025
[138, 120] loss: 0.027
[138, 180] loss: 0.026
[138, 240] loss: 0.026
[138, 300] loss: 0.025
[138, 360] loss: 0.028
Epoch: 138 -> Loss: 0.0104033928365
Epoch: 138 -> Test Accuracy: 92.1675
[139, 60] loss: 0.024
[139, 120] loss: 0.025
[139, 180] loss: 0.025
[139, 240] loss: 0.026
[139, 300] loss: 0.028
[139, 360] loss: 0.024
Epoch: 139 -> Loss: 0.0127109345049
Epoch: 139 -> Test Accuracy: 92.1775
[140, 60] loss: 0.021
[140, 120] loss: 0.021
[140, 180] loss: 0.026
[140, 240] loss: 0.026
[140, 300] loss: 0.025
[140, 360] loss: 0.026
Epoch: 140 -> Loss: 0.0257526282221
Epoch: 140 -> Test Accuracy: 92.115
[141, 60] loss: 0.023
[141, 120] loss: 0.023
[141, 180] loss: 0.026
[141, 240] loss: 0.026
[141, 300] loss: 0.024
[141, 360] loss: 0.027
Epoch: 141 -> Loss: 0.0222018398345
Epoch: 141 -> Test Accuracy: 92.065
[142, 60] loss: 0.024
[142, 120] loss: 0.025
[142, 180] loss: 0.026
[142, 240] loss: 0.024
[142, 300] loss: 0.025
[142, 360] loss: 0.024
Epoch: 142 -> Loss: 0.0381575599313
Epoch: 142 -> Test Accuracy: 92.0
[143, 60] loss: 0.022
[143, 120] loss: 0.026
[143, 180] loss: 0.025
[143, 240] loss: 0.027
[143, 300] loss: 0.024
[143, 360] loss: 0.026
Epoch: 143 -> Loss: 0.0457306429744
Epoch: 143 -> Test Accuracy: 92.1475
[144, 60] loss: 0.020
[144, 120] loss: 0.023
[144, 180] loss: 0.024
[144, 240] loss: 0.024
[144, 300] loss: 0.025
[144, 360] loss: 0.027
Epoch: 144 -> Loss: 0.0474306493998
Epoch: 144 -> Test Accuracy: 91.9325
[145, 60] loss: 0.022
[145, 120] loss: 0.023
[145, 180] loss: 0.023
[145, 240] loss: 0.024
[145, 300] loss: 0.024
[145, 360] loss: 0.026
Epoch: 145 -> Loss: 0.0229899715632
Epoch: 145 -> Test Accuracy: 92.1325
[146, 60] loss: 0.022
[146, 120] loss: 0.023
[146, 180] loss: 0.024
[146, 240] loss: 0.022
[146, 300] loss: 0.024
[146, 360] loss: 0.026
Epoch: 146 -> Loss: 0.0179903283715
Epoch: 146 -> Test Accuracy: 91.8925
[147, 60] loss: 0.024
[147, 120] loss: 0.025
[147, 180] loss: 0.023
[147, 240] loss: 0.024
[147, 300] loss: 0.026
[147, 360] loss: 0.025
Epoch: 147 -> Loss: 0.0645735636353
Epoch: 147 -> Test Accuracy: 91.975
[148, 60] loss: 0.023
[148, 120] loss: 0.022
[148, 180] loss: 0.023
[148, 240] loss: 0.026
[148, 300] loss: 0.023
[148, 360] loss: 0.026
Epoch: 148 -> Loss: 0.0240027327091
Epoch: 148 -> Test Accuracy: 91.765
[149, 60] loss: 0.023
[149, 120] loss: 0.021
[149, 180] loss: 0.022
[149, 240] loss: 0.025
[149, 300] loss: 0.026
[149, 360] loss: 0.026
Epoch: 149 -> Loss: 0.0485193766654
Epoch: 149 -> Test Accuracy: 92.1175
[150, 60] loss: 0.023
[150, 120] loss: 0.024
[150, 180] loss: 0.024
[150, 240] loss: 0.025
[150, 300] loss: 0.027
[150, 360] loss: 0.026
Epoch: 150 -> Loss: 0.0145528595895
Epoch: 150 -> Test Accuracy: 91.965
[151, 60] loss: 0.024
[151, 120] loss: 0.023
[151, 180] loss: 0.022
[151, 240] loss: 0.025
[151, 300] loss: 0.024
[151, 360] loss: 0.028
Epoch: 151 -> Loss: 0.0295480005443
Epoch: 151 -> Test Accuracy: 91.835
[152, 60] loss: 0.024
[152, 120] loss: 0.024
[152, 180] loss: 0.022
[152, 240] loss: 0.024
[152, 300] loss: 0.024
[152, 360] loss: 0.028
Epoch: 152 -> Loss: 0.0133929345757
Epoch: 152 -> Test Accuracy: 92.2025
[153, 60] loss: 0.024
[153, 120] loss: 0.025
[153, 180] loss: 0.026
[153, 240] loss: 0.025
[153, 300] loss: 0.027
[153, 360] loss: 0.028
Epoch: 153 -> Loss: 0.0198680497706
Epoch: 153 -> Test Accuracy: 91.95
[154, 60] loss: 0.024
[154, 120] loss: 0.024
[154, 180] loss: 0.025
[154, 240] loss: 0.024
[154, 300] loss: 0.027
[154, 360] loss: 0.026
Epoch: 154 -> Loss: 0.0222103130072
Epoch: 154 -> Test Accuracy: 91.965
[155, 60] loss: 0.022
[155, 120] loss: 0.022
[155, 180] loss: 0.024
[155, 240] loss: 0.025
[155, 300] loss: 0.029
[155, 360] loss: 0.028
Epoch: 155 -> Loss: 0.0241133067757
Epoch: 155 -> Test Accuracy: 91.935
[156, 60] loss: 0.024
[156, 120] loss: 0.026
[156, 180] loss: 0.024
[156, 240] loss: 0.026
[156, 300] loss: 0.024
[156, 360] loss: 0.024
Epoch: 156 -> Loss: 0.028401767835
Epoch: 156 -> Test Accuracy: 91.945
[157, 60] loss: 0.021
[157, 120] loss: 0.025
[157, 180] loss: 0.026
[157, 240] loss: 0.024
[157, 300] loss: 0.025
[157, 360] loss: 0.025
Epoch: 157 -> Loss: 0.0401775017381
Epoch: 157 -> Test Accuracy: 91.9725
[158, 60] loss: 0.022
[158, 120] loss: 0.023
[158, 180] loss: 0.025
[158, 240] loss: 0.025
[158, 300] loss: 0.026
[158, 360] loss: 0.024
Epoch: 158 -> Loss: 0.0298565886915
Epoch: 158 -> Test Accuracy: 91.9175
[159, 60] loss: 0.022
[159, 120] loss: 0.021
[159, 180] loss: 0.024
[159, 240] loss: 0.025
[159, 300] loss: 0.024
[159, 360] loss: 0.024
Epoch: 159 -> Loss: 0.0185794588178
Epoch: 159 -> Test Accuracy: 91.9525
[160, 60] loss: 0.024
[160, 120] loss: 0.022
[160, 180] loss: 0.023
[160, 240] loss: 0.025
[160, 300] loss: 0.026
[160, 360] loss: 0.026
Epoch: 160 -> Loss: 0.0450544841588
Epoch: 160 -> Test Accuracy: 91.885
[161, 60] loss: 0.019
[161, 120] loss: 0.016
[161, 180] loss: 0.016
[161, 240] loss: 0.014
[161, 300] loss: 0.014
[161, 360] loss: 0.014
Epoch: 161 -> Loss: 0.0197333395481
Epoch: 161 -> Test Accuracy: 92.43
[162, 60] loss: 0.012
[162, 120] loss: 0.012
[162, 180] loss: 0.013
[162, 240] loss: 0.012
[162, 300] loss: 0.012
[162, 360] loss: 0.011
Epoch: 162 -> Loss: 0.0110598672181
Epoch: 162 -> Test Accuracy: 92.4975
[163, 60] loss: 0.011
[163, 120] loss: 0.010
[163, 180] loss: 0.010
[163, 240] loss: 0.011
[163, 300] loss: 0.010
[163, 360] loss: 0.011
Epoch: 163 -> Loss: 0.00951013527811
Epoch: 163 -> Test Accuracy: 92.415
[164, 60] loss: 0.010
[164, 120] loss: 0.010
[164, 180] loss: 0.011
[164, 240] loss: 0.010
[164, 300] loss: 0.009
[164, 360] loss: 0.011
Epoch: 164 -> Loss: 0.0117461169139
Epoch: 164 -> Test Accuracy: 92.51
[165, 60] loss: 0.009
[165, 120] loss: 0.009
[165, 180] loss: 0.010
[165, 240] loss: 0.011
[165, 300] loss: 0.009
[165, 360] loss: 0.010
Epoch: 165 -> Loss: 0.00752267753705
Epoch: 165 -> Test Accuracy: 92.5775
[166, 60] loss: 0.009
[166, 120] loss: 0.008
[166, 180] loss: 0.010
[166, 240] loss: 0.010
[166, 300] loss: 0.009
[166, 360] loss: 0.008
Epoch: 166 -> Loss: 0.0189105384052
Epoch: 166 -> Test Accuracy: 92.48
[167, 60] loss: 0.009
[167, 120] loss: 0.009
[167, 180] loss: 0.008
[167, 240] loss: 0.009
[167, 300] loss: 0.009
[167, 360] loss: 0.007
Epoch: 167 -> Loss: 0.00751985330135
Epoch: 167 -> Test Accuracy: 92.585
[168, 60] loss: 0.008
[168, 120] loss: 0.008
[168, 180] loss: 0.008
[168, 240] loss: 0.009
[168, 300] loss: 0.008
[168, 360] loss: 0.009
Epoch: 168 -> Loss: 0.0115286121145
Epoch: 168 -> Test Accuracy: 92.5175
[169, 60] loss: 0.007
[169, 120] loss: 0.009
[169, 180] loss: 0.007
[169, 240] loss: 0.009
[169, 300] loss: 0.008
[169, 360] loss: 0.008
Epoch: 169 -> Loss: 0.00585957989097
Epoch: 169 -> Test Accuracy: 92.595
[170, 60] loss: 0.008
[170, 120] loss: 0.008
[170, 180] loss: 0.008
[170, 240] loss: 0.009
[170, 300] loss: 0.008
[170, 360] loss: 0.007
Epoch: 170 -> Loss: 0.0213364996016
Epoch: 170 -> Test Accuracy: 92.485
[171, 60] loss: 0.008
[171, 120] loss: 0.008
[171, 180] loss: 0.007
[171, 240] loss: 0.008
[171, 300] loss: 0.007
[171, 360] loss: 0.008
Epoch: 171 -> Loss: 0.00604963768274
Epoch: 171 -> Test Accuracy: 92.5125
[172, 60] loss: 0.007
[172, 120] loss: 0.007
[172, 180] loss: 0.008
[172, 240] loss: 0.008
[172, 300] loss: 0.007
[172, 360] loss: 0.008
Epoch: 172 -> Loss: 0.0104969134554
Epoch: 172 -> Test Accuracy: 92.4425
[173, 60] loss: 0.008
[173, 120] loss: 0.008
[173, 180] loss: 0.007
[173, 240] loss: 0.008
[173, 300] loss: 0.007
[173, 360] loss: 0.007
Epoch: 173 -> Loss: 0.00278425146826
Epoch: 173 -> Test Accuracy: 92.4725
[174, 60] loss: 0.006
[174, 120] loss: 0.007
[174, 180] loss: 0.006
[174, 240] loss: 0.007
[174, 300] loss: 0.007
[174, 360] loss: 0.007
Epoch: 174 -> Loss: 0.00542673934251
Epoch: 174 -> Test Accuracy: 92.4625
[175, 60] loss: 0.007
[175, 120] loss: 0.007
[175, 180] loss: 0.007
[175, 240] loss: 0.006
[175, 300] loss: 0.006
[175, 360] loss: 0.007
Epoch: 175 -> Loss: 0.00884590484202
Epoch: 175 -> Test Accuracy: 92.495
[176, 60] loss: 0.007
[176, 120] loss: 0.006
[176, 180] loss: 0.007
[176, 240] loss: 0.007
[176, 300] loss: 0.006
[176, 360] loss: 0.006
Epoch: 176 -> Loss: 0.0113233039156
Epoch: 176 -> Test Accuracy: 92.5075
[177, 60] loss: 0.007
[177, 120] loss: 0.006
[177, 180] loss: 0.006
[177, 240] loss: 0.006
[177, 300] loss: 0.007
[177, 360] loss: 0.008
Epoch: 177 -> Loss: 0.00249320571311
Epoch: 177 -> Test Accuracy: 92.5775
[178, 60] loss: 0.007
[178, 120] loss: 0.007
[178, 180] loss: 0.007
[178, 240] loss: 0.006
[178, 300] loss: 0.007
[178, 360] loss: 0.006
Epoch: 178 -> Loss: 0.00494795758277
Epoch: 178 -> Test Accuracy: 92.485
[179, 60] loss: 0.006
[179, 120] loss: 0.006
[179, 180] loss: 0.006
[179, 240] loss: 0.007
[179, 300] loss: 0.007
[179, 360] loss: 0.006
Epoch: 179 -> Loss: 0.00537769403309
Epoch: 179 -> Test Accuracy: 92.5
[180, 60] loss: 0.006
[180, 120] loss: 0.007
[180, 180] loss: 0.006
[180, 240] loss: 0.007
[180, 300] loss: 0.006
[180, 360] loss: 0.006
Epoch: 180 -> Loss: 0.00434305658564
Epoch: 180 -> Test Accuracy: 92.535
[181, 60] loss: 0.006
[181, 120] loss: 0.007
[181, 180] loss: 0.006
[181, 240] loss: 0.007
[181, 300] loss: 0.007
[181, 360] loss: 0.006
Epoch: 181 -> Loss: 0.00808005221188
Epoch: 181 -> Test Accuracy: 92.5275
[182, 60] loss: 0.006
[182, 120] loss: 0.006
[182, 180] loss: 0.006
[182, 240] loss: 0.006
[182, 300] loss: 0.006
[182, 360] loss: 0.006
Epoch: 182 -> Loss: 0.00897836498916
Epoch: 182 -> Test Accuracy: 92.6
[183, 60] loss: 0.006
[183, 120] loss: 0.006
[183, 180] loss: 0.006
[183, 240] loss: 0.006
[183, 300] loss: 0.006
[183, 360] loss: 0.007
Epoch: 183 -> Loss: 0.00255749886855
Epoch: 183 -> Test Accuracy: 92.47
[184, 60] loss: 0.006
[184, 120] loss: 0.006
[184, 180] loss: 0.006
[184, 240] loss: 0.006
[184, 300] loss: 0.006
[184, 360] loss: 0.006
Epoch: 184 -> Loss: 0.00525060575455
Epoch: 184 -> Test Accuracy: 92.475
[185, 60] loss: 0.006
[185, 120] loss: 0.006
[185, 180] loss: 0.006
[185, 240] loss: 0.006
[185, 300] loss: 0.007
[185, 360] loss: 0.007
Epoch: 185 -> Loss: 0.0120564447716
Epoch: 185 -> Test Accuracy: 92.4775
[186, 60] loss: 0.005
[186, 120] loss: 0.006
[186, 180] loss: 0.006
[186, 240] loss: 0.006
[186, 300] loss: 0.006
[186, 360] loss: 0.006
Epoch: 186 -> Loss: 0.00262727355585
Epoch: 186 -> Test Accuracy: 92.445
[187, 60] loss: 0.006
[187, 120] loss: 0.006
[187, 180] loss: 0.005
[187, 240] loss: 0.006
[187, 300] loss: 0.006
[187, 360] loss: 0.006
Epoch: 187 -> Loss: 0.00634869094938
Epoch: 187 -> Test Accuracy: 92.42
[188, 60] loss: 0.006
[188, 120] loss: 0.006
[188, 180] loss: 0.005
[188, 240] loss: 0.005
[188, 300] loss: 0.006
[188, 360] loss: 0.006
Epoch: 188 -> Loss: 0.0084777334705
Epoch: 188 -> Test Accuracy: 92.455
[189, 60] loss: 0.006
[189, 120] loss: 0.005
[189, 180] loss: 0.006
[189, 240] loss: 0.006
[189, 300] loss: 0.006
[189, 360] loss: 0.005
Epoch: 189 -> Loss: 0.00381430843845
Epoch: 189 -> Test Accuracy: 92.47
[190, 60] loss: 0.006
[190, 120] loss: 0.006
[190, 180] loss: 0.005
[190, 240] loss: 0.005
[190, 300] loss: 0.006
[190, 360] loss: 0.005
Epoch: 190 -> Loss: 0.00268593197688
Epoch: 190 -> Test Accuracy: 92.51
[191, 60] loss: 0.006
[191, 120] loss: 0.006
[191, 180] loss: 0.006
[191, 240] loss: 0.005
[191, 300] loss: 0.005
[191, 360] loss: 0.006
Epoch: 191 -> Loss: 0.0037877925206
Epoch: 191 -> Test Accuracy: 92.545
[192, 60] loss: 0.005
[192, 120] loss: 0.005
[192, 180] loss: 0.006
[192, 240] loss: 0.005
[192, 300] loss: 0.005
[192, 360] loss: 0.006
Epoch: 192 -> Loss: 0.0051264828071
Epoch: 192 -> Test Accuracy: 92.4
[193, 60] loss: 0.005
[193, 120] loss: 0.005
[193, 180] loss: 0.006
[193, 240] loss: 0.006
[193, 300] loss: 0.006
[193, 360] loss: 0.006
Epoch: 193 -> Loss: 0.00503174727783
Epoch: 193 -> Test Accuracy: 92.445
[194, 60] loss: 0.005
[194, 120] loss: 0.005
[194, 180] loss: 0.005
[194, 240] loss: 0.006
[194, 300] loss: 0.006
[194, 360] loss: 0.005
Epoch: 194 -> Loss: 0.00384769542143
Epoch: 194 -> Test Accuracy: 92.54
[195, 60] loss: 0.006
[195, 120] loss: 0.005
[195, 180] loss: 0.006
[195, 240] loss: 0.006
[195, 300] loss: 0.005
[195, 360] loss: 0.005
Epoch: 195 -> Loss: 0.00383017142303
Epoch: 195 -> Test Accuracy: 92.5175
[196, 60] loss: 0.005
[196, 120] loss: 0.005
[196, 180] loss: 0.006
[196, 240] loss: 0.005
[196, 300] loss: 0.005
[196, 360] loss: 0.005
Epoch: 196 -> Loss: 0.0110269840807
Epoch: 196 -> Test Accuracy: 92.535
[197, 60] loss: 0.005
[197, 120] loss: 0.005
[197, 180] loss: 0.006
[197, 240] loss: 0.005
[197, 300] loss: 0.006
[197, 360] loss: 0.005
Epoch: 197 -> Loss: 0.00754360621795
Epoch: 197 -> Test Accuracy: 92.5475
[198, 60] loss: 0.005
[198, 120] loss: 0.006
[198, 180] loss: 0.005
[198, 240] loss: 0.005
[198, 300] loss: 0.006
[198, 360] loss: 0.005
Epoch: 198 -> Loss: 0.0103376815096
Epoch: 198 -> Test Accuracy: 92.6025
[199, 60] loss: 0.005
[199, 120] loss: 0.005
[199, 180] loss: 0.005
[199, 240] loss: 0.005
[199, 300] loss: 0.006
[199, 360] loss: 0.006
Epoch: 199 -> Loss: 0.00404023518786
Epoch: 199 -> Test Accuracy: 92.64
[200, 60] loss: 0.006
[200, 120] loss: 0.005
[200, 180] loss: 0.005
[200, 240] loss: 0.005
[200, 300] loss: 0.005
[200, 360] loss: 0.005
Epoch: 200 -> Loss: 0.00244236225262
Epoch: 200 -> Test Accuracy: 92.6325
Finished Training
In [8]:
# train NonLinearClassifiers on feature map of net_3block
block4_loss_log, _, block4_test_accuracy_log, _, _ = tr.train_all_blocks(4, 10, [0.1, 0.02, 0.004, 0.0008], 
    [20, 40, 45, 100], 0.9, 5e-4, net_block4, criterion, trainloader, None, testloader) 
[1, 60] loss: 2.230
[1, 120] loss: 1.261
[1, 180] loss: 1.156
[1, 240] loss: 1.095
[1, 300] loss: 1.038
[1, 360] loss: 1.018
Epoch: 1 -> Loss: 0.952750682831
Epoch: 1 -> Test Accuracy: 67.22
[2, 60] loss: 0.948
[2, 120] loss: 0.923
[2, 180] loss: 0.898
[2, 240] loss: 0.926
[2, 300] loss: 0.883
[2, 360] loss: 0.866
Epoch: 2 -> Loss: 0.915363311768
Epoch: 2 -> Test Accuracy: 70.55
[3, 60] loss: 0.825
[3, 120] loss: 0.847
[3, 180] loss: 0.832
[3, 240] loss: 0.823
[3, 300] loss: 0.791
[3, 360] loss: 0.810
Epoch: 3 -> Loss: 0.695132553577
Epoch: 3 -> Test Accuracy: 73.29
[4, 60] loss: 0.773
[4, 120] loss: 0.784
[4, 180] loss: 0.756
[4, 240] loss: 0.784
[4, 300] loss: 0.762
[4, 360] loss: 0.753
Epoch: 4 -> Loss: 0.705285429955
Epoch: 4 -> Test Accuracy: 73.98
[5, 60] loss: 0.727
[5, 120] loss: 0.750
[5, 180] loss: 0.763
[5, 240] loss: 0.721
[5, 300] loss: 0.723
[5, 360] loss: 0.717
Epoch: 5 -> Loss: 0.739547371864
Epoch: 5 -> Test Accuracy: 75.62
[6, 60] loss: 0.721
[6, 120] loss: 0.724
[6, 180] loss: 0.711
[6, 240] loss: 0.686
[6, 300] loss: 0.712
[6, 360] loss: 0.714
Epoch: 6 -> Loss: 0.644628822803
Epoch: 6 -> Test Accuracy: 76.28
[7, 60] loss: 0.690
[7, 120] loss: 0.687
[7, 180] loss: 0.693
[7, 240] loss: 0.694
[7, 300] loss: 0.687
[7, 360] loss: 0.693
Epoch: 7 -> Loss: 0.792090713978
Epoch: 7 -> Test Accuracy: 76.84
[8, 60] loss: 0.661
[8, 120] loss: 0.686
[8, 180] loss: 0.675
[8, 240] loss: 0.682
[8, 300] loss: 0.680
[8, 360] loss: 0.691
Epoch: 8 -> Loss: 0.655223548412
Epoch: 8 -> Test Accuracy: 77.01
[9, 60] loss: 0.650
[9, 120] loss: 0.663
[9, 180] loss: 0.669
[9, 240] loss: 0.655
[9, 300] loss: 0.657
[9, 360] loss: 0.676
Epoch: 9 -> Loss: 0.560119509697
Epoch: 9 -> Test Accuracy: 77.28
[10, 60] loss: 0.642
[10, 120] loss: 0.629
[10, 180] loss: 0.654
[10, 240] loss: 0.669
[10, 300] loss: 0.662
[10, 360] loss: 0.669
Epoch: 10 -> Loss: 0.82323038578
Epoch: 10 -> Test Accuracy: 77.14
[11, 60] loss: 0.643
[11, 120] loss: 0.632
[11, 180] loss: 0.653
[11, 240] loss: 0.662
[11, 300] loss: 0.641
[11, 360] loss: 0.632
Epoch: 11 -> Loss: 0.432897984982
Epoch: 11 -> Test Accuracy: 78.05
[12, 60] loss: 0.632
[12, 120] loss: 0.654
[12, 180] loss: 0.639
[12, 240] loss: 0.633
[12, 300] loss: 0.639
[12, 360] loss: 0.641
Epoch: 12 -> Loss: 0.653931498528
Epoch: 12 -> Test Accuracy: 78.27
[13, 60] loss: 0.603
[13, 120] loss: 0.627
[13, 180] loss: 0.619
[13, 240] loss: 0.654
[13, 300] loss: 0.644
[13, 360] loss: 0.640
Epoch: 13 -> Loss: 0.710495054722
Epoch: 13 -> Test Accuracy: 78.23
[14, 60] loss: 0.633
[14, 120] loss: 0.618
[14, 180] loss: 0.613
[14, 240] loss: 0.637
[14, 300] loss: 0.633
[14, 360] loss: 0.656
Epoch: 14 -> Loss: 0.647937297821
Epoch: 14 -> Test Accuracy: 78.51
[15, 60] loss: 0.613
[15, 120] loss: 0.609
[15, 180] loss: 0.621
[15, 240] loss: 0.637
[15, 300] loss: 0.627
[15, 360] loss: 0.629
Epoch: 15 -> Loss: 0.76855301857
Epoch: 15 -> Test Accuracy: 78.02
[16, 60] loss: 0.606
[16, 120] loss: 0.629
[16, 180] loss: 0.621
[16, 240] loss: 0.595
[16, 300] loss: 0.620
[16, 360] loss: 0.647
Epoch: 16 -> Loss: 0.796156644821
Epoch: 16 -> Test Accuracy: 78.29
[17, 60] loss: 0.602
[17, 120] loss: 0.599
[17, 180] loss: 0.622
[17, 240] loss: 0.638
[17, 300] loss: 0.627
[17, 360] loss: 0.620
Epoch: 17 -> Loss: 0.669784069061
Epoch: 17 -> Test Accuracy: 78.06
[18, 60] loss: 0.598
[18, 120] loss: 0.610
[18, 180] loss: 0.604
[18, 240] loss: 0.603
[18, 300] loss: 0.635
[18, 360] loss: 0.634
Epoch: 18 -> Loss: 0.724856078625
Epoch: 18 -> Test Accuracy: 78.13
[19, 60] loss: 0.588
[19, 120] loss: 0.603
[19, 180] loss: 0.626
[19, 240] loss: 0.625
[19, 300] loss: 0.598
[19, 360] loss: 0.619
Epoch: 19 -> Loss: 0.865086734295
Epoch: 19 -> Test Accuracy: 78.23
[20, 60] loss: 0.592
[20, 120] loss: 0.606
[20, 180] loss: 0.617
[20, 240] loss: 0.602
[20, 300] loss: 0.616
[20, 360] loss: 0.637
Epoch: 20 -> Loss: 0.652887940407
Epoch: 20 -> Test Accuracy: 79.02
[21, 60] loss: 0.556
[21, 120] loss: 0.510
[21, 180] loss: 0.510
[21, 240] loss: 0.502
[21, 300] loss: 0.501
[21, 360] loss: 0.504
Epoch: 21 -> Loss: 0.482809841633
Epoch: 21 -> Test Accuracy: 81.46
[22, 60] loss: 0.464
[22, 120] loss: 0.485
[22, 180] loss: 0.473
[22, 240] loss: 0.482
[22, 300] loss: 0.465
[22, 360] loss: 0.486
Epoch: 22 -> Loss: 0.485416263342
Epoch: 22 -> Test Accuracy: 81.38
[23, 60] loss: 0.441
[23, 120] loss: 0.456
[23, 180] loss: 0.465
[23, 240] loss: 0.470
[23, 300] loss: 0.446
[23, 360] loss: 0.464
Epoch: 23 -> Loss: 0.383998095989
Epoch: 23 -> Test Accuracy: 81.89
[24, 60] loss: 0.440
[24, 120] loss: 0.444
[24, 180] loss: 0.447
[24, 240] loss: 0.456
[24, 300] loss: 0.459
[24, 360] loss: 0.452
Epoch: 24 -> Loss: 0.478100955486
Epoch: 24 -> Test Accuracy: 81.64
[25, 60] loss: 0.444
[25, 120] loss: 0.430
[25, 180] loss: 0.439
[25, 240] loss: 0.432
[25, 300] loss: 0.448
[25, 360] loss: 0.445
Epoch: 25 -> Loss: 0.58848541975
Epoch: 25 -> Test Accuracy: 81.59
[26, 60] loss: 0.420
[26, 120] loss: 0.424
[26, 180] loss: 0.440
[26, 240] loss: 0.433
[26, 300] loss: 0.430
[26, 360] loss: 0.436
Epoch: 26 -> Loss: 0.395174086094
Epoch: 26 -> Test Accuracy: 81.59
[27, 60] loss: 0.425
[27, 120] loss: 0.422
[27, 180] loss: 0.410
[27, 240] loss: 0.418
[27, 300] loss: 0.420
[27, 360] loss: 0.424
Epoch: 27 -> Loss: 0.599011421204
Epoch: 27 -> Test Accuracy: 81.62
[28, 60] loss: 0.413
[28, 120] loss: 0.419
[28, 180] loss: 0.413
[28, 240] loss: 0.423
[28, 300] loss: 0.435
[28, 360] loss: 0.429
Epoch: 28 -> Loss: 0.480876982212
Epoch: 28 -> Test Accuracy: 81.92
[29, 60] loss: 0.404
[29, 120] loss: 0.402
[29, 180] loss: 0.425
[29, 240] loss: 0.409
[29, 300] loss: 0.420
[29, 360] loss: 0.434
Epoch: 29 -> Loss: 0.37843477726
Epoch: 29 -> Test Accuracy: 81.7
[30, 60] loss: 0.421
[30, 120] loss: 0.407
[30, 180] loss: 0.405
[30, 240] loss: 0.399
[30, 300] loss: 0.424
[30, 360] loss: 0.415
Epoch: 30 -> Loss: 0.355725944042
Epoch: 30 -> Test Accuracy: 81.94
[31, 60] loss: 0.402
[31, 120] loss: 0.411
[31, 180] loss: 0.425
[31, 240] loss: 0.420
[31, 300] loss: 0.423
[31, 360] loss: 0.416
Epoch: 31 -> Loss: 0.418404638767
Epoch: 31 -> Test Accuracy: 82.11
[32, 60] loss: 0.422
[32, 120] loss: 0.402
[32, 180] loss: 0.410
[32, 240] loss: 0.397
[32, 300] loss: 0.415
[32, 360] loss: 0.425
Epoch: 32 -> Loss: 0.333952903748
Epoch: 32 -> Test Accuracy: 82.3
[33, 60] loss: 0.388
[33, 120] loss: 0.405
[33, 180] loss: 0.405
[33, 240] loss: 0.416
[33, 300] loss: 0.418
[33, 360] loss: 0.425
Epoch: 33 -> Loss: 0.731445670128
Epoch: 33 -> Test Accuracy: 81.97
[34, 60] loss: 0.397
[34, 120] loss: 0.385
[34, 180] loss: 0.425
[34, 240] loss: 0.406
[34, 300] loss: 0.407
[34, 360] loss: 0.415
Epoch: 34 -> Loss: 0.553569555283
Epoch: 34 -> Test Accuracy: 81.59
[35, 60] loss: 0.397
[35, 120] loss: 0.395
[35, 180] loss: 0.407
[35, 240] loss: 0.398
[35, 300] loss: 0.418
[35, 360] loss: 0.427
Epoch: 35 -> Loss: 0.419232696295
Epoch: 35 -> Test Accuracy: 81.89
[36, 60] loss: 0.415
[36, 120] loss: 0.402
[36, 180] loss: 0.385
[36, 240] loss: 0.387
[36, 300] loss: 0.397
[36, 360] loss: 0.397
Epoch: 36 -> Loss: 0.358517944813
Epoch: 36 -> Test Accuracy: 81.42
[37, 60] loss: 0.407
[37, 120] loss: 0.389
[37, 180] loss: 0.388
[37, 240] loss: 0.413
[37, 300] loss: 0.403
[37, 360] loss: 0.404
Epoch: 37 -> Loss: 0.481689363718
Epoch: 37 -> Test Accuracy: 81.52
[38, 60] loss: 0.390
[38, 120] loss: 0.404
[38, 180] loss: 0.417
[38, 240] loss: 0.406
[38, 300] loss: 0.397
[38, 360] loss: 0.419
Epoch: 38 -> Loss: 0.464514911175
Epoch: 38 -> Test Accuracy: 81.76
[39, 60] loss: 0.407
[39, 120] loss: 0.400
[39, 180] loss: 0.391
[39, 240] loss: 0.392
[39, 300] loss: 0.401
[39, 360] loss: 0.418
Epoch: 39 -> Loss: 0.431790441275
Epoch: 39 -> Test Accuracy: 81.0
[40, 60] loss: 0.380
[40, 120] loss: 0.392
[40, 180] loss: 0.404
[40, 240] loss: 0.398
[40, 300] loss: 0.424
[40, 360] loss: 0.412
Epoch: 40 -> Loss: 0.348495423794
Epoch: 40 -> Test Accuracy: 81.21
[41, 60] loss: 0.367
[41, 120] loss: 0.362
[41, 180] loss: 0.366
[41, 240] loss: 0.346
[41, 300] loss: 0.357
[41, 360] loss: 0.344
Epoch: 41 -> Loss: 0.338767826557
Epoch: 41 -> Test Accuracy: 82.69
[42, 60] loss: 0.332
[42, 120] loss: 0.328
[42, 180] loss: 0.324
[42, 240] loss: 0.348
[42, 300] loss: 0.337
[42, 360] loss: 0.324
Epoch: 42 -> Loss: 0.255785286427
Epoch: 42 -> Test Accuracy: 82.87
[43, 60] loss: 0.332
[43, 120] loss: 0.317
[43, 180] loss: 0.317
[43, 240] loss: 0.316
[43, 300] loss: 0.325
[43, 360] loss: 0.323
Epoch: 43 -> Loss: 0.271632134914
Epoch: 43 -> Test Accuracy: 82.77
[44, 60] loss: 0.330
[44, 120] loss: 0.324
[44, 180] loss: 0.328
[44, 240] loss: 0.304
[44, 300] loss: 0.319
[44, 360] loss: 0.306
Epoch: 44 -> Loss: 0.272089123726
Epoch: 44 -> Test Accuracy: 82.87
[45, 60] loss: 0.310
[45, 120] loss: 0.312
[45, 180] loss: 0.300
[45, 240] loss: 0.311
[45, 300] loss: 0.306
[45, 360] loss: 0.311
Epoch: 45 -> Loss: 0.231358855963
Epoch: 45 -> Test Accuracy: 82.81
[46, 60] loss: 0.289
[46, 120] loss: 0.314
[46, 180] loss: 0.315
[46, 240] loss: 0.302
[46, 300] loss: 0.289
[46, 360] loss: 0.301
Epoch: 46 -> Loss: 0.206209033728
Epoch: 46 -> Test Accuracy: 82.97
[47, 60] loss: 0.293
[47, 120] loss: 0.298
[47, 180] loss: 0.289
[47, 240] loss: 0.302
[47, 300] loss: 0.288
[47, 360] loss: 0.296
Epoch: 47 -> Loss: 0.462675005198
Epoch: 47 -> Test Accuracy: 82.83
[48, 60] loss: 0.293
[48, 120] loss: 0.296
[48, 180] loss: 0.289
[48, 240] loss: 0.298
[48, 300] loss: 0.287
[48, 360] loss: 0.283
Epoch: 48 -> Loss: 0.299165278673
Epoch: 48 -> Test Accuracy: 82.99
[49, 60] loss: 0.274
[49, 120] loss: 0.292
[49, 180] loss: 0.288
[49, 240] loss: 0.293
[49, 300] loss: 0.287
[49, 360] loss: 0.297
Epoch: 49 -> Loss: 0.173502907157
Epoch: 49 -> Test Accuracy: 83.16
[50, 60] loss: 0.288
[50, 120] loss: 0.282
[50, 180] loss: 0.273
[50, 240] loss: 0.290
[50, 300] loss: 0.294
[50, 360] loss: 0.286
Epoch: 50 -> Loss: 0.494676679373
Epoch: 50 -> Test Accuracy: 83.08
[51, 60] loss: 0.290
[51, 120] loss: 0.284
[51, 180] loss: 0.283
[51, 240] loss: 0.290
[51, 300] loss: 0.293
[51, 360] loss: 0.275
Epoch: 51 -> Loss: 0.411444842815
Epoch: 51 -> Test Accuracy: 83.12
[52, 60] loss: 0.284
[52, 120] loss: 0.278
[52, 180] loss: 0.300
[52, 240] loss: 0.293
[52, 300] loss: 0.270
[52, 360] loss: 0.276
Epoch: 52 -> Loss: 0.199544996023
Epoch: 52 -> Test Accuracy: 83.23
[53, 60] loss: 0.287
[53, 120] loss: 0.280
[53, 180] loss: 0.289
[53, 240] loss: 0.288
[53, 300] loss: 0.285
[53, 360] loss: 0.290
Epoch: 53 -> Loss: 0.387977451086
Epoch: 53 -> Test Accuracy: 83.07
[54, 60] loss: 0.263
[54, 120] loss: 0.291
[54, 180] loss: 0.286
[54, 240] loss: 0.273
[54, 300] loss: 0.306
[54, 360] loss: 0.282
Epoch: 54 -> Loss: 0.397488355637
Epoch: 54 -> Test Accuracy: 83.16
[55, 60] loss: 0.292
[55, 120] loss: 0.288
[55, 180] loss: 0.276
[55, 240] loss: 0.265
[55, 300] loss: 0.274
[55, 360] loss: 0.267
Epoch: 55 -> Loss: 0.258340984583
Epoch: 55 -> Test Accuracy: 83.17
[56, 60] loss: 0.284
[56, 120] loss: 0.268
[56, 180] loss: 0.280
[56, 240] loss: 0.282
[56, 300] loss: 0.279
[56, 360] loss: 0.272
Epoch: 56 -> Loss: 0.32501745224
Epoch: 56 -> Test Accuracy: 83.29
[57, 60] loss: 0.263
[57, 120] loss: 0.275
[57, 180] loss: 0.272
[57, 240] loss: 0.279
[57, 300] loss: 0.291
[57, 360] loss: 0.279
Epoch: 57 -> Loss: 0.295313954353
Epoch: 57 -> Test Accuracy: 83.35
[58, 60] loss: 0.276
[58, 120] loss: 0.277
[58, 180] loss: 0.272
[58, 240] loss: 0.259
[58, 300] loss: 0.274
[58, 360] loss: 0.267
Epoch: 58 -> Loss: 0.275536715984
Epoch: 58 -> Test Accuracy: 83.25
[59, 60] loss: 0.272
[59, 120] loss: 0.274
[59, 180] loss: 0.291
[59, 240] loss: 0.280
[59, 300] loss: 0.272
[59, 360] loss: 0.275
Epoch: 59 -> Loss: 0.297197401524
Epoch: 59 -> Test Accuracy: 83.2
[60, 60] loss: 0.273
[60, 120] loss: 0.280
[60, 180] loss: 0.284
[60, 240] loss: 0.279
[60, 300] loss: 0.277
[60, 360] loss: 0.258
Epoch: 60 -> Loss: 0.14818200469
Epoch: 60 -> Test Accuracy: 83.14
[61, 60] loss: 0.277
[61, 120] loss: 0.273
[61, 180] loss: 0.280
[61, 240] loss: 0.281
[61, 300] loss: 0.272
[61, 360] loss: 0.275
Epoch: 61 -> Loss: 0.30923396349
Epoch: 61 -> Test Accuracy: 83.18
[62, 60] loss: 0.266
[62, 120] loss: 0.260
[62, 180] loss: 0.271
[62, 240] loss: 0.273
[62, 300] loss: 0.270
[62, 360] loss: 0.291
Epoch: 62 -> Loss: 0.328295975924
Epoch: 62 -> Test Accuracy: 83.0
[63, 60] loss: 0.264
[63, 120] loss: 0.271
[63, 180] loss: 0.275
[63, 240] loss: 0.277
[63, 300] loss: 0.266
[63, 360] loss: 0.269
Epoch: 63 -> Loss: 0.30826947093
Epoch: 63 -> Test Accuracy: 83.21
[64, 60] loss: 0.266
[64, 120] loss: 0.265
[64, 180] loss: 0.292
[64, 240] loss: 0.264
[64, 300] loss: 0.276
[64, 360] loss: 0.274
Epoch: 64 -> Loss: 0.30528062582
Epoch: 64 -> Test Accuracy: 83.03
[65, 60] loss: 0.284
[65, 120] loss: 0.262
[65, 180] loss: 0.264
[65, 240] loss: 0.270
[65, 300] loss: 0.266
[65, 360] loss: 0.274
Epoch: 65 -> Loss: 0.249018624425
Epoch: 65 -> Test Accuracy: 83.16
[66, 60] loss: 0.268
[66, 120] loss: 0.277
[66, 180] loss: 0.259
[66, 240] loss: 0.281
[66, 300] loss: 0.261
[66, 360] loss: 0.274
Epoch: 66 -> Loss: 0.274273097515
Epoch: 66 -> Test Accuracy: 83.16
[67, 60] loss: 0.263
[67, 120] loss: 0.260
[67, 180] loss: 0.280
[67, 240] loss: 0.273
[67, 300] loss: 0.256
[67, 360] loss: 0.267
Epoch: 67 -> Loss: 0.17559632659
Epoch: 67 -> Test Accuracy: 83.24
[68, 60] loss: 0.265
[68, 120] loss: 0.260
[68, 180] loss: 0.259
[68, 240] loss: 0.273
[68, 300] loss: 0.271
[68, 360] loss: 0.274
Epoch: 68 -> Loss: 0.307023853064
Epoch: 68 -> Test Accuracy: 83.29
[69, 60] loss: 0.258
[69, 120] loss: 0.261
[69, 180] loss: 0.264
[69, 240] loss: 0.265
[69, 300] loss: 0.278
[69, 360] loss: 0.262
Epoch: 69 -> Loss: 0.280606687069
Epoch: 69 -> Test Accuracy: 83.34
[70, 60] loss: 0.264
[70, 120] loss: 0.263
[70, 180] loss: 0.256
[70, 240] loss: 0.269
[70, 300] loss: 0.256
[70, 360] loss: 0.275
Epoch: 70 -> Loss: 0.251718312502
Epoch: 70 -> Test Accuracy: 83.33
[71, 60] loss: 0.261
[71, 120] loss: 0.259
[71, 180] loss: 0.263
[71, 240] loss: 0.270
[71, 300] loss: 0.266
[71, 360] loss: 0.257
Epoch: 71 -> Loss: 0.174997925758
Epoch: 71 -> Test Accuracy: 83.24
[72, 60] loss: 0.256
[72, 120] loss: 0.257
[72, 180] loss: 0.255
[72, 240] loss: 0.268
[72, 300] loss: 0.265
[72, 360] loss: 0.253
Epoch: 72 -> Loss: 0.235711380839
Epoch: 72 -> Test Accuracy: 83.09
[73, 60] loss: 0.254
[73, 120] loss: 0.255
[73, 180] loss: 0.264
[73, 240] loss: 0.264
[73, 300] loss: 0.277
[73, 360] loss: 0.262
Epoch: 73 -> Loss: 0.318305492401
Epoch: 73 -> Test Accuracy: 83.21
[74, 60] loss: 0.266
[74, 120] loss: 0.255
[74, 180] loss: 0.256
[74, 240] loss: 0.262
[74, 300] loss: 0.266
[74, 360] loss: 0.258
Epoch: 74 -> Loss: 0.349063485861
Epoch: 74 -> Test Accuracy: 83.21
[75, 60] loss: 0.247
[75, 120] loss: 0.255
[75, 180] loss: 0.253
[75, 240] loss: 0.254
[75, 300] loss: 0.269
[75, 360] loss: 0.265
Epoch: 75 -> Loss: 0.211874723434
Epoch: 75 -> Test Accuracy: 83.07
[76, 60] loss: 0.252
[76, 120] loss: 0.263
[76, 180] loss: 0.258
[76, 240] loss: 0.257
[76, 300] loss: 0.267
[76, 360] loss: 0.261
Epoch: 76 -> Loss: 0.393870055676
Epoch: 76 -> Test Accuracy: 83.02
[77, 60] loss: 0.258
[77, 120] loss: 0.256
[77, 180] loss: 0.259
[77, 240] loss: 0.253
[77, 300] loss: 0.270
[77, 360] loss: 0.255
Epoch: 77 -> Loss: 0.254414737225
Epoch: 77 -> Test Accuracy: 83.17
[78, 60] loss: 0.248
[78, 120] loss: 0.264
[78, 180] loss: 0.256
[78, 240] loss: 0.251
[78, 300] loss: 0.262
[78, 360] loss: 0.249
Epoch: 78 -> Loss: 0.285253852606
Epoch: 78 -> Test Accuracy: 83.13
[79, 60] loss: 0.254
[79, 120] loss: 0.248
[79, 180] loss: 0.262
[79, 240] loss: 0.267
[79, 300] loss: 0.248
[79, 360] loss: 0.248
Epoch: 79 -> Loss: 0.229285866022
Epoch: 79 -> Test Accuracy: 83.03
[80, 60] loss: 0.272
[80, 120] loss: 0.261
[80, 180] loss: 0.259
[80, 240] loss: 0.258
[80, 300] loss: 0.254
[80, 360] loss: 0.249
Epoch: 80 -> Loss: 0.279805004597
Epoch: 80 -> Test Accuracy: 82.96
[81, 60] loss: 0.251
[81, 120] loss: 0.251
[81, 180] loss: 0.250
[81, 240] loss: 0.256
[81, 300] loss: 0.259
[81, 360] loss: 0.247
Epoch: 81 -> Loss: 0.243606477976
Epoch: 81 -> Test Accuracy: 82.93
[82, 60] loss: 0.249
[82, 120] loss: 0.248
[82, 180] loss: 0.254
[82, 240] loss: 0.252
[82, 300] loss: 0.249
[82, 360] loss: 0.251
Epoch: 82 -> Loss: 0.286044418812
Epoch: 82 -> Test Accuracy: 83.18
[83, 60] loss: 0.256
[83, 120] loss: 0.253
[83, 180] loss: 0.258
[83, 240] loss: 0.255
[83, 300] loss: 0.249
[83, 360] loss: 0.242
Epoch: 83 -> Loss: 0.435457646847
Epoch: 83 -> Test Accuracy: 83.14
[84, 60] loss: 0.259
[84, 120] loss: 0.260
[84, 180] loss: 0.248
[84, 240] loss: 0.250
[84, 300] loss: 0.261
[84, 360] loss: 0.249
Epoch: 84 -> Loss: 0.239048808813
Epoch: 84 -> Test Accuracy: 83.15
[85, 60] loss: 0.262
[85, 120] loss: 0.244
[85, 180] loss: 0.252
[85, 240] loss: 0.256
[85, 300] loss: 0.252
[85, 360] loss: 0.245
Epoch: 85 -> Loss: 0.258603096008
Epoch: 85 -> Test Accuracy: 83.1
[86, 60] loss: 0.247
[86, 120] loss: 0.259
[86, 180] loss: 0.251
[86, 240] loss: 0.243
[86, 300] loss: 0.245
[86, 360] loss: 0.257
Epoch: 86 -> Loss: 0.231459587812
Epoch: 86 -> Test Accuracy: 82.98
[87, 60] loss: 0.246
[87, 120] loss: 0.248
[87, 180] loss: 0.243
[87, 240] loss: 0.247
[87, 300] loss: 0.259
[87, 360] loss: 0.238
Epoch: 87 -> Loss: 0.371732950211
Epoch: 87 -> Test Accuracy: 83.05
[88, 60] loss: 0.241
[88, 120] loss: 0.249
[88, 180] loss: 0.246
[88, 240] loss: 0.243
[88, 300] loss: 0.248
[88, 360] loss: 0.245
Epoch: 88 -> Loss: 0.245003938675
Epoch: 88 -> Test Accuracy: 83.12
[89, 60] loss: 0.244
[89, 120] loss: 0.239
[89, 180] loss: 0.256
[89, 240] loss: 0.243
[89, 300] loss: 0.239
[89, 360] loss: 0.242
Epoch: 89 -> Loss: 0.161676540971
Epoch: 89 -> Test Accuracy: 82.96
[90, 60] loss: 0.242
[90, 120] loss: 0.244
[90, 180] loss: 0.244
[90, 240] loss: 0.245
[90, 300] loss: 0.241
[90, 360] loss: 0.243
Epoch: 90 -> Loss: 0.201568320394
Epoch: 90 -> Test Accuracy: 82.87
[91, 60] loss: 0.244
[91, 120] loss: 0.226
[91, 180] loss: 0.240
[91, 240] loss: 0.252
[91, 300] loss: 0.245
[91, 360] loss: 0.250
Epoch: 91 -> Loss: 0.181910797954
Epoch: 91 -> Test Accuracy: 83.01
[92, 60] loss: 0.249
[92, 120] loss: 0.247
[92, 180] loss: 0.248
[92, 240] loss: 0.235
[92, 300] loss: 0.252
[92, 360] loss: 0.231
Epoch: 92 -> Loss: 0.263022780418
Epoch: 92 -> Test Accuracy: 83.03
[93, 60] loss: 0.243
[93, 120] loss: 0.249
[93, 180] loss: 0.241
[93, 240] loss: 0.245
[93, 300] loss: 0.246
[93, 360] loss: 0.253
Epoch: 93 -> Loss: 0.235726192594
Epoch: 93 -> Test Accuracy: 83.1
[94, 60] loss: 0.240
[94, 120] loss: 0.246
[94, 180] loss: 0.244
[94, 240] loss: 0.239
[94, 300] loss: 0.252
[94, 360] loss: 0.234
Epoch: 94 -> Loss: 0.18844217062
Epoch: 94 -> Test Accuracy: 82.92
[95, 60] loss: 0.234
[95, 120] loss: 0.240
[95, 180] loss: 0.245
[95, 240] loss: 0.244
[95, 300] loss: 0.236
[95, 360] loss: 0.247
Epoch: 95 -> Loss: 0.238631889224
Epoch: 95 -> Test Accuracy: 83.03
[96, 60] loss: 0.234
[96, 120] loss: 0.235
[96, 180] loss: 0.254
[96, 240] loss: 0.242
[96, 300] loss: 0.233
[96, 360] loss: 0.242
Epoch: 96 -> Loss: 0.326489895582
Epoch: 96 -> Test Accuracy: 83.05
[97, 60] loss: 0.232
[97, 120] loss: 0.233
[97, 180] loss: 0.238
[97, 240] loss: 0.247
[97, 300] loss: 0.233
[97, 360] loss: 0.233
Epoch: 97 -> Loss: 0.166981846094
Epoch: 97 -> Test Accuracy: 83.03
[98, 60] loss: 0.254
[98, 120] loss: 0.245
[98, 180] loss: 0.228
[98, 240] loss: 0.236
[98, 300] loss: 0.223
[98, 360] loss: 0.234
Epoch: 98 -> Loss: 0.200633198023
Epoch: 98 -> Test Accuracy: 83.22
[99, 60] loss: 0.239
[99, 120] loss: 0.242
[99, 180] loss: 0.234
[99, 240] loss: 0.234
[99, 300] loss: 0.232
[99, 360] loss: 0.237
Epoch: 99 -> Loss: 0.202944040298
Epoch: 99 -> Test Accuracy: 83.3
[100, 60] loss: 0.239
[100, 120] loss: 0.232
[100, 180] loss: 0.238
[100, 240] loss: 0.232
[100, 300] loss: 0.231
[100, 360] loss: 0.242
Epoch: 100 -> Loss: 0.19259929657
Epoch: 100 -> Test Accuracy: 83.12
Finished Training
[1, 60] loss: 1.694
[1, 120] loss: 0.854
[1, 180] loss: 0.739
[1, 240] loss: 0.710
[1, 300] loss: 0.680
[1, 360] loss: 0.640
Epoch: 1 -> Loss: 0.636373102665
Epoch: 1 -> Test Accuracy: 79.01
[2, 60] loss: 0.574
[2, 120] loss: 0.599
[2, 180] loss: 0.579
[2, 240] loss: 0.561
[2, 300] loss: 0.572
[2, 360] loss: 0.563
Epoch: 2 -> Loss: 0.623220920563
Epoch: 2 -> Test Accuracy: 80.69
[3, 60] loss: 0.516
[3, 120] loss: 0.521
[3, 180] loss: 0.526
[3, 240] loss: 0.504
[3, 300] loss: 0.506
[3, 360] loss: 0.519
Epoch: 3 -> Loss: 0.387173980474
Epoch: 3 -> Test Accuracy: 81.6
[4, 60] loss: 0.482
[4, 120] loss: 0.480
[4, 180] loss: 0.460
[4, 240] loss: 0.482
[4, 300] loss: 0.479
[4, 360] loss: 0.472
Epoch: 4 -> Loss: 0.515356242657
Epoch: 4 -> Test Accuracy: 83.1
[5, 60] loss: 0.448
[5, 120] loss: 0.443
[5, 180] loss: 0.442
[5, 240] loss: 0.449
[5, 300] loss: 0.459
[5, 360] loss: 0.454
Epoch: 5 -> Loss: 0.620316267014
Epoch: 5 -> Test Accuracy: 82.64
[6, 60] loss: 0.433
[6, 120] loss: 0.421
[6, 180] loss: 0.452
[6, 240] loss: 0.436
[6, 300] loss: 0.440
[6, 360] loss: 0.444
Epoch: 6 -> Loss: 0.475322335958
Epoch: 6 -> Test Accuracy: 82.88
[7, 60] loss: 0.411
[7, 120] loss: 0.427
[7, 180] loss: 0.422
[7, 240] loss: 0.420
[7, 300] loss: 0.433
[7, 360] loss: 0.438
Epoch: 7 -> Loss: 0.601088821888
Epoch: 7 -> Test Accuracy: 83.59
[8, 60] loss: 0.430
[8, 120] loss: 0.398
[8, 180] loss: 0.408
[8, 240] loss: 0.416
[8, 300] loss: 0.419
[8, 360] loss: 0.431
Epoch: 8 -> Loss: 0.451033890247
Epoch: 8 -> Test Accuracy: 83.35
[9, 60] loss: 0.388
[9, 120] loss: 0.391
[9, 180] loss: 0.421
[9, 240] loss: 0.417
[9, 300] loss: 0.428
[9, 360] loss: 0.423
Epoch: 9 -> Loss: 0.294439375401
Epoch: 9 -> Test Accuracy: 83.37
[10, 60] loss: 0.384
[10, 120] loss: 0.392
[10, 180] loss: 0.407
[10, 240] loss: 0.400
[10, 300] loss: 0.408
[10, 360] loss: 0.416
Epoch: 10 -> Loss: 0.517010211945
Epoch: 10 -> Test Accuracy: 83.87
[11, 60] loss: 0.380
[11, 120] loss: 0.376
[11, 180] loss: 0.406
[11, 240] loss: 0.406
[11, 300] loss: 0.401
[11, 360] loss: 0.411
Epoch: 11 -> Loss: 0.370214641094
Epoch: 11 -> Test Accuracy: 84.18
[12, 60] loss: 0.388
[12, 120] loss: 0.393
[12, 180] loss: 0.393
[12, 240] loss: 0.385
[12, 300] loss: 0.406
[12, 360] loss: 0.388
Epoch: 12 -> Loss: 0.292529672384
Epoch: 12 -> Test Accuracy: 84.02
[13, 60] loss: 0.374
[13, 120] loss: 0.389
[13, 180] loss: 0.379
[13, 240] loss: 0.412
[13, 300] loss: 0.384
[13, 360] loss: 0.383
Epoch: 13 -> Loss: 0.447354316711
Epoch: 13 -> Test Accuracy: 83.77
[14, 60] loss: 0.348
[14, 120] loss: 0.391
[14, 180] loss: 0.389
[14, 240] loss: 0.394
[14, 300] loss: 0.398
[14, 360] loss: 0.398
Epoch: 14 -> Loss: 0.337503701448
Epoch: 14 -> Test Accuracy: 83.95
[15, 60] loss: 0.355
[15, 120] loss: 0.364
[15, 180] loss: 0.384
[15, 240] loss: 0.395
[15, 300] loss: 0.379
[15, 360] loss: 0.397
Epoch: 15 -> Loss: 0.357359498739
Epoch: 15 -> Test Accuracy: 84.22
[16, 60] loss: 0.360
[16, 120] loss: 0.380
[16, 180] loss: 0.368
[16, 240] loss: 0.387
[16, 300] loss: 0.373
[16, 360] loss: 0.403
Epoch: 16 -> Loss: 0.50570756197
Epoch: 16 -> Test Accuracy: 84.19
[17, 60] loss: 0.354
[17, 120] loss: 0.354
[17, 180] loss: 0.385
[17, 240] loss: 0.372
[17, 300] loss: 0.394
[17, 360] loss: 0.402
Epoch: 17 -> Loss: 0.459813982248
Epoch: 17 -> Test Accuracy: 83.83
[18, 60] loss: 0.346
[18, 120] loss: 0.362
[18, 180] loss: 0.375
[18, 240] loss: 0.378
[18, 300] loss: 0.392
[18, 360] loss: 0.377
Epoch: 18 -> Loss: 0.312015414238
Epoch: 18 -> Test Accuracy: 83.88
[19, 60] loss: 0.359
[19, 120] loss: 0.364
[19, 180] loss: 0.369
[19, 240] loss: 0.379
[19, 300] loss: 0.374
[19, 360] loss: 0.387
Epoch: 19 -> Loss: 0.488042891026
Epoch: 19 -> Test Accuracy: 83.76
[20, 60] loss: 0.348
[20, 120] loss: 0.375
[20, 180] loss: 0.365
[20, 240] loss: 0.376
[20, 300] loss: 0.371
[20, 360] loss: 0.403
Epoch: 20 -> Loss: 0.45441275835
Epoch: 20 -> Test Accuracy: 84.08
[21, 60] loss: 0.325
[21, 120] loss: 0.298
[21, 180] loss: 0.300
[21, 240] loss: 0.286
[21, 300] loss: 0.295
[21, 360] loss: 0.271
Epoch: 21 -> Loss: 0.223467111588
Epoch: 21 -> Test Accuracy: 86.25
[22, 60] loss: 0.252
[22, 120] loss: 0.256
[22, 180] loss: 0.279
[22, 240] loss: 0.269
[22, 300] loss: 0.258
[22, 360] loss: 0.248
Epoch: 22 -> Loss: 0.456174194813
Epoch: 22 -> Test Accuracy: 86.18
[23, 60] loss: 0.250
[23, 120] loss: 0.243
[23, 180] loss: 0.254
[23, 240] loss: 0.240
[23, 300] loss: 0.257
[23, 360] loss: 0.256
Epoch: 23 -> Loss: 0.266110479832
Epoch: 23 -> Test Accuracy: 86.31
[24, 60] loss: 0.231
[24, 120] loss: 0.232
[24, 180] loss: 0.230
[24, 240] loss: 0.256
[24, 300] loss: 0.232
[24, 360] loss: 0.242
Epoch: 24 -> Loss: 0.224151775241
Epoch: 24 -> Test Accuracy: 86.27
[25, 60] loss: 0.227
[25, 120] loss: 0.220
[25, 180] loss: 0.232
[25, 240] loss: 0.215
[25, 300] loss: 0.238
[25, 360] loss: 0.231
Epoch: 25 -> Loss: 0.272114276886
Epoch: 25 -> Test Accuracy: 86.17
[26, 60] loss: 0.204
[26, 120] loss: 0.209
[26, 180] loss: 0.221
[26, 240] loss: 0.216
[26, 300] loss: 0.228
[26, 360] loss: 0.235
Epoch: 26 -> Loss: 0.226827740669
Epoch: 26 -> Test Accuracy: 86.28
[27, 60] loss: 0.222
[27, 120] loss: 0.220
[27, 180] loss: 0.218
[27, 240] loss: 0.210
[27, 300] loss: 0.214
[27, 360] loss: 0.232
Epoch: 27 -> Loss: 0.18773420155
Epoch: 27 -> Test Accuracy: 86.02
[28, 60] loss: 0.207
[28, 120] loss: 0.215
[28, 180] loss: 0.223
[28, 240] loss: 0.207
[28, 300] loss: 0.220
[28, 360] loss: 0.220
Epoch: 28 -> Loss: 0.34678658843
Epoch: 28 -> Test Accuracy: 85.59
[29, 60] loss: 0.191
[29, 120] loss: 0.216
[29, 180] loss: 0.225
[29, 240] loss: 0.210
[29, 300] loss: 0.204
[29, 360] loss: 0.213
Epoch: 29 -> Loss: 0.256274312735
Epoch: 29 -> Test Accuracy: 85.9
[30, 60] loss: 0.193
[30, 120] loss: 0.201
[30, 180] loss: 0.209
[30, 240] loss: 0.210
[30, 300] loss: 0.202
[30, 360] loss: 0.208
Epoch: 30 -> Loss: 0.269257485867
Epoch: 30 -> Test Accuracy: 85.51
[31, 60] loss: 0.192
[31, 120] loss: 0.196
[31, 180] loss: 0.199
[31, 240] loss: 0.203
[31, 300] loss: 0.206
[31, 360] loss: 0.218
Epoch: 31 -> Loss: 0.22524318099
Epoch: 31 -> Test Accuracy: 85.74
[32, 60] loss: 0.199
[32, 120] loss: 0.196
[32, 180] loss: 0.195
[32, 240] loss: 0.197
[32, 300] loss: 0.205
[32, 360] loss: 0.204
Epoch: 32 -> Loss: 0.305242300034
Epoch: 32 -> Test Accuracy: 85.9
[33, 60] loss: 0.192
[33, 120] loss: 0.209
[33, 180] loss: 0.216
[33, 240] loss: 0.191
[33, 300] loss: 0.209
[33, 360] loss: 0.206
Epoch: 33 -> Loss: 0.265587151051
Epoch: 33 -> Test Accuracy: 85.81
[34, 60] loss: 0.203
[34, 120] loss: 0.199
[34, 180] loss: 0.213
[34, 240] loss: 0.204
[34, 300] loss: 0.208
[34, 360] loss: 0.202
Epoch: 34 -> Loss: 0.298242270947
Epoch: 34 -> Test Accuracy: 85.5
[35, 60] loss: 0.191
[35, 120] loss: 0.201
[35, 180] loss: 0.204
[35, 240] loss: 0.199
[35, 300] loss: 0.209
[35, 360] loss: 0.217
Epoch: 35 -> Loss: 0.330903589725
Epoch: 35 -> Test Accuracy: 85.81
[36, 60] loss: 0.193
[36, 120] loss: 0.200
[36, 180] loss: 0.198
[36, 240] loss: 0.197
[36, 300] loss: 0.202
[36, 360] loss: 0.204
Epoch: 36 -> Loss: 0.358889877796
Epoch: 36 -> Test Accuracy: 85.75
[37, 60] loss: 0.186
[37, 120] loss: 0.189
[37, 180] loss: 0.195
[37, 240] loss: 0.210
[37, 300] loss: 0.193
[37, 360] loss: 0.205
Epoch: 37 -> Loss: 0.110308326781
Epoch: 37 -> Test Accuracy: 85.42
[38, 60] loss: 0.182
[38, 120] loss: 0.185
[38, 180] loss: 0.187
[38, 240] loss: 0.205
[38, 300] loss: 0.197
[38, 360] loss: 0.204
Epoch: 38 -> Loss: 0.329784154892
Epoch: 38 -> Test Accuracy: 85.51
[39, 60] loss: 0.196
[39, 120] loss: 0.190
[39, 180] loss: 0.189
[39, 240] loss: 0.185
[39, 300] loss: 0.197
[39, 360] loss: 0.204
Epoch: 39 -> Loss: 0.115293264389
Epoch: 39 -> Test Accuracy: 85.29
[40, 60] loss: 0.190
[40, 120] loss: 0.180
[40, 180] loss: 0.200
[40, 240] loss: 0.203
[40, 300] loss: 0.201
[40, 360] loss: 0.206
Epoch: 40 -> Loss: 0.194905668497
Epoch: 40 -> Test Accuracy: 85.65
[41, 60] loss: 0.173
[41, 120] loss: 0.170
[41, 180] loss: 0.160
[41, 240] loss: 0.168
[41, 300] loss: 0.168
[41, 360] loss: 0.159
Epoch: 41 -> Loss: 0.138016298413
Epoch: 41 -> Test Accuracy: 86.18
[42, 60] loss: 0.155
[42, 120] loss: 0.152
[42, 180] loss: 0.153
[42, 240] loss: 0.140
[42, 300] loss: 0.148
[42, 360] loss: 0.151
Epoch: 42 -> Loss: 0.0941276699305
Epoch: 42 -> Test Accuracy: 86.37
[43, 60] loss: 0.128
[43, 120] loss: 0.136
[43, 180] loss: 0.145
[43, 240] loss: 0.135
[43, 300] loss: 0.141
[43, 360] loss: 0.137
Epoch: 43 -> Loss: 0.142272397876
Epoch: 43 -> Test Accuracy: 86.57
[44, 60] loss: 0.133
[44, 120] loss: 0.128
[44, 180] loss: 0.118
[44, 240] loss: 0.131
[44, 300] loss: 0.136
[44, 360] loss: 0.141
Epoch: 44 -> Loss: 0.19723905623
Epoch: 44 -> Test Accuracy: 86.43
[45, 60] loss: 0.125
[45, 120] loss: 0.128
[45, 180] loss: 0.131
[45, 240] loss: 0.125
[45, 300] loss: 0.126
[45, 360] loss: 0.124
Epoch: 45 -> Loss: 0.06195602566
Epoch: 45 -> Test Accuracy: 86.59
[46, 60] loss: 0.120
[46, 120] loss: 0.117
[46, 180] loss: 0.127
[46, 240] loss: 0.120
[46, 300] loss: 0.124
[46, 360] loss: 0.125
Epoch: 46 -> Loss: 0.147839203477
Epoch: 46 -> Test Accuracy: 86.53
[47, 60] loss: 0.121
[47, 120] loss: 0.107
[47, 180] loss: 0.106
[47, 240] loss: 0.121
[47, 300] loss: 0.125
[47, 360] loss: 0.123
Epoch: 47 -> Loss: 0.202754691243
Epoch: 47 -> Test Accuracy: 86.58
[48, 60] loss: 0.123
[48, 120] loss: 0.104
[48, 180] loss: 0.114
[48, 240] loss: 0.117
[48, 300] loss: 0.127
[48, 360] loss: 0.116
Epoch: 48 -> Loss: 0.117970824242
Epoch: 48 -> Test Accuracy: 86.55
[49, 60] loss: 0.120
[49, 120] loss: 0.116
[49, 180] loss: 0.109
[49, 240] loss: 0.122
[49, 300] loss: 0.118
[49, 360] loss: 0.119
Epoch: 49 -> Loss: 0.12256872654
Epoch: 49 -> Test Accuracy: 86.55
[50, 60] loss: 0.120
[50, 120] loss: 0.116
[50, 180] loss: 0.115
[50, 240] loss: 0.121
[50, 300] loss: 0.108
[50, 360] loss: 0.114
Epoch: 50 -> Loss: 0.0400307402015
Epoch: 50 -> Test Accuracy: 86.63
[51, 60] loss: 0.117
[51, 120] loss: 0.102
[51, 180] loss: 0.111
[51, 240] loss: 0.114
[51, 300] loss: 0.111
[51, 360] loss: 0.107
Epoch: 51 -> Loss: 0.0920950621367
Epoch: 51 -> Test Accuracy: 86.6
[52, 60] loss: 0.113
[52, 120] loss: 0.106
[52, 180] loss: 0.106
[52, 240] loss: 0.112
[52, 300] loss: 0.115
[52, 360] loss: 0.114
Epoch: 52 -> Loss: 0.114840552211
Epoch: 52 -> Test Accuracy: 86.66
[53, 60] loss: 0.111
[53, 120] loss: 0.101
[53, 180] loss: 0.108
[53, 240] loss: 0.114
[53, 300] loss: 0.115
[53, 360] loss: 0.102
Epoch: 53 -> Loss: 0.0632527545094
Epoch: 53 -> Test Accuracy: 86.68
[54, 60] loss: 0.105
[54, 120] loss: 0.112
[54, 180] loss: 0.111
[54, 240] loss: 0.113
[54, 300] loss: 0.103
[54, 360] loss: 0.102
Epoch: 54 -> Loss: 0.251395791769
Epoch: 54 -> Test Accuracy: 86.68
[55, 60] loss: 0.107
[55, 120] loss: 0.103
[55, 180] loss: 0.110
[55, 240] loss: 0.105
[55, 300] loss: 0.115
[55, 360] loss: 0.102
Epoch: 55 -> Loss: 0.0681119412184
Epoch: 55 -> Test Accuracy: 86.67
[56, 60] loss: 0.103
[56, 120] loss: 0.105
[56, 180] loss: 0.104
[56, 240] loss: 0.107
[56, 300] loss: 0.111
[56, 360] loss: 0.104
Epoch: 56 -> Loss: 0.149298399687
Epoch: 56 -> Test Accuracy: 86.62
[57, 60] loss: 0.115
[57, 120] loss: 0.105
[57, 180] loss: 0.112
[57, 240] loss: 0.104
[57, 300] loss: 0.107
[57, 360] loss: 0.102
Epoch: 57 -> Loss: 0.127135068178
Epoch: 57 -> Test Accuracy: 86.62
[58, 60] loss: 0.105
[58, 120] loss: 0.103
[58, 180] loss: 0.102
[58, 240] loss: 0.104
[58, 300] loss: 0.103
[58, 360] loss: 0.102
Epoch: 58 -> Loss: 0.0885294824839
Epoch: 58 -> Test Accuracy: 86.71
[59, 60] loss: 0.105
[59, 120] loss: 0.109
[59, 180] loss: 0.103
[59, 240] loss: 0.108
[59, 300] loss: 0.103
[59, 360] loss: 0.099
Epoch: 59 -> Loss: 0.122068703175
Epoch: 59 -> Test Accuracy: 86.67
[60, 60] loss: 0.104
[60, 120] loss: 0.109
[60, 180] loss: 0.100
[60, 240] loss: 0.099
[60, 300] loss: 0.101
[60, 360] loss: 0.105
Epoch: 60 -> Loss: 0.0522882454097
Epoch: 60 -> Test Accuracy: 86.72
[61, 60] loss: 0.104
[61, 120] loss: 0.105
[61, 180] loss: 0.104
[61, 240] loss: 0.101
[61, 300] loss: 0.100
[61, 360] loss: 0.104
Epoch: 61 -> Loss: 0.0897772461176
Epoch: 61 -> Test Accuracy: 86.79
[62, 60] loss: 0.106
[62, 120] loss: 0.097
[62, 180] loss: 0.099
[62, 240] loss: 0.095
[62, 300] loss: 0.101
[62, 360] loss: 0.098
Epoch: 62 -> Loss: 0.102323554456
Epoch: 62 -> Test Accuracy: 86.58
[63, 60] loss: 0.101
[63, 120] loss: 0.097
[63, 180] loss: 0.094
[63, 240] loss: 0.107
[63, 300] loss: 0.099
[63, 360] loss: 0.097
Epoch: 63 -> Loss: 0.12129150331
Epoch: 63 -> Test Accuracy: 86.56
[64, 60] loss: 0.093
[64, 120] loss: 0.097
[64, 180] loss: 0.098
[64, 240] loss: 0.098
[64, 300] loss: 0.098
[64, 360] loss: 0.097
Epoch: 64 -> Loss: 0.160067588091
Epoch: 64 -> Test Accuracy: 86.48
[65, 60] loss: 0.095
[65, 120] loss: 0.099
[65, 180] loss: 0.100
[65, 240] loss: 0.096
[65, 300] loss: 0.091
[65, 360] loss: 0.095
Epoch: 65 -> Loss: 0.128874734044
Epoch: 65 -> Test Accuracy: 86.46
[66, 60] loss: 0.093
[66, 120] loss: 0.097
[66, 180] loss: 0.098
[66, 240] loss: 0.093
[66, 300] loss: 0.097
[66, 360] loss: 0.102
Epoch: 66 -> Loss: 0.112519145012
Epoch: 66 -> Test Accuracy: 86.64
[67, 60] loss: 0.103
[67, 120] loss: 0.097
[67, 180] loss: 0.101
[67, 240] loss: 0.101
[67, 300] loss: 0.096
[67, 360] loss: 0.096
Epoch: 67 -> Loss: 0.145581796765
Epoch: 67 -> Test Accuracy: 86.67
[68, 60] loss: 0.096
[68, 120] loss: 0.093
[68, 180] loss: 0.096
[68, 240] loss: 0.097
[68, 300] loss: 0.090
[68, 360] loss: 0.102
Epoch: 68 -> Loss: 0.0466391667724
Epoch: 68 -> Test Accuracy: 86.58
[69, 60] loss: 0.092
[69, 120] loss: 0.094
[69, 180] loss: 0.100
[69, 240] loss: 0.091
[69, 300] loss: 0.101
[69, 360] loss: 0.096
Epoch: 69 -> Loss: 0.125960662961
Epoch: 69 -> Test Accuracy: 86.5
[70, 60] loss: 0.090
[70, 120] loss: 0.097
[70, 180] loss: 0.096
[70, 240] loss: 0.102
[70, 300] loss: 0.098
[70, 360] loss: 0.098
Epoch: 70 -> Loss: 0.143998235464
Epoch: 70 -> Test Accuracy: 86.55
[71, 60] loss: 0.092
[71, 120] loss: 0.103
[71, 180] loss: 0.095
[71, 240] loss: 0.097
[71, 300] loss: 0.088
[71, 360] loss: 0.092
Epoch: 71 -> Loss: 0.0564641170204
Epoch: 71 -> Test Accuracy: 86.44
[72, 60] loss: 0.095
[72, 120] loss: 0.084
[72, 180] loss: 0.103
[72, 240] loss: 0.090
[72, 300] loss: 0.098
[72, 360] loss: 0.096
Epoch: 72 -> Loss: 0.122200034559
Epoch: 72 -> Test Accuracy: 86.66
[73, 60] loss: 0.089
[73, 120] loss: 0.091
[73, 180] loss: 0.089
[73, 240] loss: 0.095
[73, 300] loss: 0.094
[73, 360] loss: 0.089
Epoch: 73 -> Loss: 0.175251856446
Epoch: 73 -> Test Accuracy: 86.49
[74, 60] loss: 0.089
[74, 120] loss: 0.088
[74, 180] loss: 0.088
[74, 240] loss: 0.093
[74, 300] loss: 0.096
[74, 360] loss: 0.092
Epoch: 74 -> Loss: 0.109243236482
Epoch: 74 -> Test Accuracy: 86.51
[75, 60] loss: 0.091
[75, 120] loss: 0.089
[75, 180] loss: 0.086
[75, 240] loss: 0.093
[75, 300] loss: 0.094
[75, 360] loss: 0.084
Epoch: 75 -> Loss: 0.157480970025
Epoch: 75 -> Test Accuracy: 86.42
[76, 60] loss: 0.088
[76, 120] loss: 0.088
[76, 180] loss: 0.092
[76, 240] loss: 0.087
[76, 300] loss: 0.094
[76, 360] loss: 0.096
Epoch: 76 -> Loss: 0.143909007311
Epoch: 76 -> Test Accuracy: 86.48
[77, 60] loss: 0.087
[77, 120] loss: 0.093
[77, 180] loss: 0.088
[77, 240] loss: 0.086
[77, 300] loss: 0.087
[77, 360] loss: 0.088
Epoch: 77 -> Loss: 0.0574901998043
Epoch: 77 -> Test Accuracy: 86.53
[78, 60] loss: 0.092
[78, 120] loss: 0.099
[78, 180] loss: 0.089
[78, 240] loss: 0.090
[78, 300] loss: 0.090
[78, 360] loss: 0.087
Epoch: 78 -> Loss: 0.195210769773
Epoch: 78 -> Test Accuracy: 86.44
[79, 60] loss: 0.089
[79, 120] loss: 0.085
[79, 180] loss: 0.090
[79, 240] loss: 0.087
[79, 300] loss: 0.089
[79, 360] loss: 0.084
Epoch: 79 -> Loss: 0.0632743090391
Epoch: 79 -> Test Accuracy: 86.65
[80, 60] loss: 0.086
[80, 120] loss: 0.091
[80, 180] loss: 0.085
[80, 240] loss: 0.088
[80, 300] loss: 0.089
[80, 360] loss: 0.084
Epoch: 80 -> Loss: 0.0598878189921
Epoch: 80 -> Test Accuracy: 86.67
[81, 60] loss: 0.094
[81, 120] loss: 0.080
[81, 180] loss: 0.079
[81, 240] loss: 0.087
[81, 300] loss: 0.084
[81, 360] loss: 0.088
Epoch: 81 -> Loss: 0.0422129034996
Epoch: 81 -> Test Accuracy: 86.62
[82, 60] loss: 0.081
[82, 120] loss: 0.084
[82, 180] loss: 0.088
[82, 240] loss: 0.085
[82, 300] loss: 0.087
[82, 360] loss: 0.090
Epoch: 82 -> Loss: 0.220996469259
Epoch: 82 -> Test Accuracy: 86.54
[83, 60] loss: 0.091
[83, 120] loss: 0.091
[83, 180] loss: 0.083
[83, 240] loss: 0.087
[83, 300] loss: 0.086
[83, 360] loss: 0.087
Epoch: 83 -> Loss: 0.0822087526321
Epoch: 83 -> Test Accuracy: 86.47
[84, 60] loss: 0.085
[84, 120] loss: 0.084
[84, 180] loss: 0.090
[84, 240] loss: 0.088
[84, 300] loss: 0.087
[84, 360] loss: 0.096
Epoch: 84 -> Loss: 0.0934516340494
Epoch: 84 -> Test Accuracy: 86.48
[85, 60] loss: 0.088
[85, 120] loss: 0.090
[85, 180] loss: 0.092
[85, 240] loss: 0.082
[85, 300] loss: 0.088
[85, 360] loss: 0.082
Epoch: 85 -> Loss: 0.0341459698975
Epoch: 85 -> Test Accuracy: 86.39
[86, 60] loss: 0.086
[86, 120] loss: 0.086
[86, 180] loss: 0.086
[86, 240] loss: 0.090
[86, 300] loss: 0.079
[86, 360] loss: 0.086
Epoch: 86 -> Loss: 0.0619657225907
Epoch: 86 -> Test Accuracy: 86.41
[87, 60] loss: 0.080
[87, 120] loss: 0.081
[87, 180] loss: 0.085
[87, 240] loss: 0.089
[87, 300] loss: 0.090
[87, 360] loss: 0.086
Epoch: 87 -> Loss: 0.102124616504
Epoch: 87 -> Test Accuracy: 86.45
[88, 60] loss: 0.080
[88, 120] loss: 0.085
[88, 180] loss: 0.074
[88, 240] loss: 0.082
[88, 300] loss: 0.085
[88, 360] loss: 0.075
Epoch: 88 -> Loss: 0.0687177181244
Epoch: 88 -> Test Accuracy: 86.45
[89, 60] loss: 0.084
[89, 120] loss: 0.088
[89, 180] loss: 0.080
[89, 240] loss: 0.085
[89, 300] loss: 0.079
[89, 360] loss: 0.080
Epoch: 89 -> Loss: 0.0518793687224
Epoch: 89 -> Test Accuracy: 86.44
[90, 60] loss: 0.080
[90, 120] loss: 0.083
[90, 180] loss: 0.077
[90, 240] loss: 0.075
[90, 300] loss: 0.080
[90, 360] loss: 0.086
Epoch: 90 -> Loss: 0.0901532620192
Epoch: 90 -> Test Accuracy: 86.43
[91, 60] loss: 0.076
[91, 120] loss: 0.082
[91, 180] loss: 0.085
[91, 240] loss: 0.083
[91, 300] loss: 0.081
[91, 360] loss: 0.079
Epoch: 91 -> Loss: 0.0522777028382
Epoch: 91 -> Test Accuracy: 86.52
[92, 60] loss: 0.074
[92, 120] loss: 0.081
[92, 180] loss: 0.074
[92, 240] loss: 0.084
[92, 300] loss: 0.079
[92, 360] loss: 0.081
Epoch: 92 -> Loss: 0.0731507614255
Epoch: 92 -> Test Accuracy: 86.48
[93, 60] loss: 0.083
[93, 120] loss: 0.078
[93, 180] loss: 0.073
[93, 240] loss: 0.080
[93, 300] loss: 0.076
[93, 360] loss: 0.082
Epoch: 93 -> Loss: 0.129418283701
Epoch: 93 -> Test Accuracy: 86.52
[94, 60] loss: 0.084
[94, 120] loss: 0.080
[94, 180] loss: 0.078
[94, 240] loss: 0.082
[94, 300] loss: 0.084
[94, 360] loss: 0.076
Epoch: 94 -> Loss: 0.0669811069965
Epoch: 94 -> Test Accuracy: 86.49
[95, 60] loss: 0.078
[95, 120] loss: 0.079
[95, 180] loss: 0.087
[95, 240] loss: 0.078
[95, 300] loss: 0.072
[95, 360] loss: 0.079
Epoch: 95 -> Loss: 0.0709091946483
Epoch: 95 -> Test Accuracy: 86.5
[96, 60] loss: 0.076
[96, 120] loss: 0.073
[96, 180] loss: 0.079
[96, 240] loss: 0.082
[96, 300] loss: 0.080
[96, 360] loss: 0.085
Epoch: 96 -> Loss: 0.0867193639278
Epoch: 96 -> Test Accuracy: 86.54
[97, 60] loss: 0.083
[97, 120] loss: 0.084
[97, 180] loss: 0.075
[97, 240] loss: 0.077
[97, 300] loss: 0.076
[97, 360] loss: 0.073
Epoch: 97 -> Loss: 0.0352884605527
Epoch: 97 -> Test Accuracy: 86.65
[98, 60] loss: 0.081
[98, 120] loss: 0.081
[98, 180] loss: 0.084
[98, 240] loss: 0.075
[98, 300] loss: 0.074
[98, 360] loss: 0.077
Epoch: 98 -> Loss: 0.0382921583951
Epoch: 98 -> Test Accuracy: 86.66
[99, 60] loss: 0.076
[99, 120] loss: 0.084
[99, 180] loss: 0.078
[99, 240] loss: 0.078
[99, 300] loss: 0.073
[99, 360] loss: 0.081
Epoch: 99 -> Loss: 0.0843428224325
Epoch: 99 -> Test Accuracy: 86.66
[100, 60] loss: 0.074
[100, 120] loss: 0.080
[100, 180] loss: 0.077
[100, 240] loss: 0.077
[100, 300] loss: 0.078
[100, 360] loss: 0.080
Epoch: 100 -> Loss: 0.116184517741
Epoch: 100 -> Test Accuracy: 86.6
Finished Training
[1, 60] loss: 1.597
[1, 120] loss: 0.856
[1, 180] loss: 0.815
[1, 240] loss: 0.771
[1, 300] loss: 0.732
[1, 360] loss: 0.709
Epoch: 1 -> Loss: 0.512435734272
Epoch: 1 -> Test Accuracy: 74.67
[2, 60] loss: 0.662
[2, 120] loss: 0.654
[2, 180] loss: 0.652
[2, 240] loss: 0.652
[2, 300] loss: 0.628
[2, 360] loss: 0.628
Epoch: 2 -> Loss: 0.862444281578
Epoch: 2 -> Test Accuracy: 77.04
[3, 60] loss: 0.599
[3, 120] loss: 0.591
[3, 180] loss: 0.597
[3, 240] loss: 0.597
[3, 300] loss: 0.584
[3, 360] loss: 0.591
Epoch: 3 -> Loss: 0.437123596668
Epoch: 3 -> Test Accuracy: 76.87
[4, 60] loss: 0.566
[4, 120] loss: 0.564
[4, 180] loss: 0.562
[4, 240] loss: 0.540
[4, 300] loss: 0.557
[4, 360] loss: 0.562
Epoch: 4 -> Loss: 0.742728292942
Epoch: 4 -> Test Accuracy: 78.66
[5, 60] loss: 0.543
[5, 120] loss: 0.561
[5, 180] loss: 0.548
[5, 240] loss: 0.532
[5, 300] loss: 0.544
[5, 360] loss: 0.529
Epoch: 5 -> Loss: 0.580091655254
Epoch: 5 -> Test Accuracy: 78.76
[6, 60] loss: 0.514
[6, 120] loss: 0.527
[6, 180] loss: 0.532
[6, 240] loss: 0.527
[6, 300] loss: 0.523
[6, 360] loss: 0.520
Epoch: 6 -> Loss: 0.561300098896
Epoch: 6 -> Test Accuracy: 78.89
[7, 60] loss: 0.481
[7, 120] loss: 0.510
[7, 180] loss: 0.518
[7, 240] loss: 0.510
[7, 300] loss: 0.522
[7, 360] loss: 0.530
Epoch: 7 -> Loss: 0.464393049479
Epoch: 7 -> Test Accuracy: 78.64
[8, 60] loss: 0.495
[8, 120] loss: 0.504
[8, 180] loss: 0.513
[8, 240] loss: 0.502
[8, 300] loss: 0.507
[8, 360] loss: 0.522
Epoch: 8 -> Loss: 0.66817688942
Epoch: 8 -> Test Accuracy: 79.35
[9, 60] loss: 0.467
[9, 120] loss: 0.507
[9, 180] loss: 0.508
[9, 240] loss: 0.508
[9, 300] loss: 0.501
[9, 360] loss: 0.512
Epoch: 9 -> Loss: 0.687930226326
Epoch: 9 -> Test Accuracy: 79.51
[10, 60] loss: 0.487
[10, 120] loss: 0.499
[10, 180] loss: 0.511
[10, 240] loss: 0.499
[10, 300] loss: 0.499
[10, 360] loss: 0.503
Epoch: 10 -> Loss: 0.542013049126
Epoch: 10 -> Test Accuracy: 79.69
[11, 60] loss: 0.495
[11, 120] loss: 0.491
[11, 180] loss: 0.496
[11, 240] loss: 0.497
[11, 300] loss: 0.517
[11, 360] loss: 0.481
Epoch: 11 -> Loss: 0.486979961395
Epoch: 11 -> Test Accuracy: 79.8
[12, 60] loss: 0.475
[12, 120] loss: 0.486
[12, 180] loss: 0.468
[12, 240] loss: 0.490
[12, 300] loss: 0.475
[12, 360] loss: 0.502
Epoch: 12 -> Loss: 0.420037835836
Epoch: 12 -> Test Accuracy: 79.45
[13, 60] loss: 0.476
[13, 120] loss: 0.463
[13, 180] loss: 0.470
[13, 240] loss: 0.486
[13, 300] loss: 0.490
[13, 360] loss: 0.503
Epoch: 13 -> Loss: 0.459505945444
Epoch: 13 -> Test Accuracy: 79.74
[14, 60] loss: 0.470
[14, 120] loss: 0.477
[14, 180] loss: 0.481
[14, 240] loss: 0.474
[14, 300] loss: 0.496
[14, 360] loss: 0.496
Epoch: 14 -> Loss: 0.476970821619
Epoch: 14 -> Test Accuracy: 79.59
[15, 60] loss: 0.468
[15, 120] loss: 0.467
[15, 180] loss: 0.460
[15, 240] loss: 0.480
[15, 300] loss: 0.511
[15, 360] loss: 0.492
Epoch: 15 -> Loss: 0.600821852684
Epoch: 15 -> Test Accuracy: 79.98
[16, 60] loss: 0.467
[16, 120] loss: 0.460
[16, 180] loss: 0.486
[16, 240] loss: 0.469
[16, 300] loss: 0.478
[16, 360] loss: 0.494
Epoch: 16 -> Loss: 0.390962034464
Epoch: 16 -> Test Accuracy: 79.33
[17, 60] loss: 0.454
[17, 120] loss: 0.459
[17, 180] loss: 0.466
[17, 240] loss: 0.482
[17, 300] loss: 0.463
[17, 360] loss: 0.487
Epoch: 17 -> Loss: 0.57130086422
Epoch: 17 -> Test Accuracy: 79.87
[18, 60] loss: 0.457
[18, 120] loss: 0.450
[18, 180] loss: 0.474
[18, 240] loss: 0.481
[18, 300] loss: 0.479
[18, 360] loss: 0.475
Epoch: 18 -> Loss: 0.460366010666
Epoch: 18 -> Test Accuracy: 79.78
[19, 60] loss: 0.441
[19, 120] loss: 0.475
[19, 180] loss: 0.476
[19, 240] loss: 0.482
[19, 300] loss: 0.494
[19, 360] loss: 0.489
Epoch: 19 -> Loss: 0.454329878092
Epoch: 19 -> Test Accuracy: 80.47
[20, 60] loss: 0.454
[20, 120] loss: 0.476
[20, 180] loss: 0.468
[20, 240] loss: 0.476
[20, 300] loss: 0.460
[20, 360] loss: 0.477
Epoch: 20 -> Loss: 0.285374134779
Epoch: 20 -> Test Accuracy: 79.99
[21, 60] loss: 0.422
[21, 120] loss: 0.410
[21, 180] loss: 0.395
[21, 240] loss: 0.400
[21, 300] loss: 0.384
[21, 360] loss: 0.395
Epoch: 21 -> Loss: 0.526229023933
Epoch: 21 -> Test Accuracy: 81.88
[22, 60] loss: 0.377
[22, 120] loss: 0.379
[22, 180] loss: 0.364
[22, 240] loss: 0.372
[22, 300] loss: 0.377
[22, 360] loss: 0.359
Epoch: 22 -> Loss: 0.288418501616
Epoch: 22 -> Test Accuracy: 82.26
[23, 60] loss: 0.362
[23, 120] loss: 0.352
[23, 180] loss: 0.360
[23, 240] loss: 0.355
[23, 300] loss: 0.363
[23, 360] loss: 0.371
Epoch: 23 -> Loss: 0.465875476599
Epoch: 23 -> Test Accuracy: 82.3
[24, 60] loss: 0.332
[24, 120] loss: 0.338
[24, 180] loss: 0.340
[24, 240] loss: 0.338
[24, 300] loss: 0.342
[24, 360] loss: 0.358
Epoch: 24 -> Loss: 0.334155619144
Epoch: 24 -> Test Accuracy: 82.27
[25, 60] loss: 0.341
[25, 120] loss: 0.355
[25, 180] loss: 0.323
[25, 240] loss: 0.338
[25, 300] loss: 0.350
[25, 360] loss: 0.344
Epoch: 25 -> Loss: 0.487725168467
Epoch: 25 -> Test Accuracy: 82.25
[26, 60] loss: 0.319
[26, 120] loss: 0.331
[26, 180] loss: 0.340
[26, 240] loss: 0.332
[26, 300] loss: 0.349
[26, 360] loss: 0.333
Epoch: 26 -> Loss: 0.412365913391
Epoch: 26 -> Test Accuracy: 82.36
[27, 60] loss: 0.320
[27, 120] loss: 0.332
[27, 180] loss: 0.323
[27, 240] loss: 0.321
[27, 300] loss: 0.336
[27, 360] loss: 0.344
Epoch: 27 -> Loss: 0.274124324322
Epoch: 27 -> Test Accuracy: 82.31
[28, 60] loss: 0.318
[28, 120] loss: 0.321
[28, 180] loss: 0.330
[28, 240] loss: 0.333
[28, 300] loss: 0.331
[28, 360] loss: 0.318
Epoch: 28 -> Loss: 0.238350436091
Epoch: 28 -> Test Accuracy: 82.14
[29, 60] loss: 0.315
[29, 120] loss: 0.318
[29, 180] loss: 0.318
[29, 240] loss: 0.304
[29, 300] loss: 0.312
[29, 360] loss: 0.336
Epoch: 29 -> Loss: 0.30804926157
Epoch: 29 -> Test Accuracy: 82.04
[30, 60] loss: 0.304
[30, 120] loss: 0.330
[30, 180] loss: 0.318
[30, 240] loss: 0.328
[30, 300] loss: 0.319
[30, 360] loss: 0.335
Epoch: 30 -> Loss: 0.315720617771
Epoch: 30 -> Test Accuracy: 81.9
[31, 60] loss: 0.316
[31, 120] loss: 0.313
[31, 180] loss: 0.317
[31, 240] loss: 0.326
[31, 300] loss: 0.327
[31, 360] loss: 0.323
Epoch: 31 -> Loss: 0.403066813946
Epoch: 31 -> Test Accuracy: 82.03
[32, 60] loss: 0.308
[32, 120] loss: 0.318
[32, 180] loss: 0.322
[32, 240] loss: 0.314
[32, 300] loss: 0.320
[32, 360] loss: 0.333
Epoch: 32 -> Loss: 0.405086994171
Epoch: 32 -> Test Accuracy: 82.0
[33, 60] loss: 0.310
[33, 120] loss: 0.307
[33, 180] loss: 0.319
[33, 240] loss: 0.329
[33, 300] loss: 0.319
[33, 360] loss: 0.320
Epoch: 33 -> Loss: 0.22546748817
Epoch: 33 -> Test Accuracy: 81.91
[34, 60] loss: 0.308
[34, 120] loss: 0.317
[34, 180] loss: 0.304
[34, 240] loss: 0.311
[34, 300] loss: 0.312
[34, 360] loss: 0.328
Epoch: 34 -> Loss: 0.248193457723
Epoch: 34 -> Test Accuracy: 81.89
[35, 60] loss: 0.301
[35, 120] loss: 0.313
[35, 180] loss: 0.315
[35, 240] loss: 0.326
[35, 300] loss: 0.318
[35, 360] loss: 0.305
Epoch: 35 -> Loss: 0.251711845398
Epoch: 35 -> Test Accuracy: 81.42
[36, 60] loss: 0.298
[36, 120] loss: 0.317
[36, 180] loss: 0.310
[36, 240] loss: 0.315
[36, 300] loss: 0.315
[36, 360] loss: 0.319
Epoch: 36 -> Loss: 0.360121488571
Epoch: 36 -> Test Accuracy: 81.77
[37, 60] loss: 0.306
[37, 120] loss: 0.319
[37, 180] loss: 0.307
[37, 240] loss: 0.308
[37, 300] loss: 0.315
[37, 360] loss: 0.307
Epoch: 37 -> Loss: 0.321190357208
Epoch: 37 -> Test Accuracy: 81.97
[38, 60] loss: 0.289
[38, 120] loss: 0.300
[38, 180] loss: 0.311
[38, 240] loss: 0.299
[38, 300] loss: 0.321
[38, 360] loss: 0.317
Epoch: 38 -> Loss: 0.319918006659
Epoch: 38 -> Test Accuracy: 81.36
[39, 60] loss: 0.297
[39, 120] loss: 0.297
[39, 180] loss: 0.303
[39, 240] loss: 0.312
[39, 300] loss: 0.297
[39, 360] loss: 0.341
Epoch: 39 -> Loss: 0.377610981464
Epoch: 39 -> Test Accuracy: 81.99
[40, 60] loss: 0.306
[40, 120] loss: 0.290
[40, 180] loss: 0.314
[40, 240] loss: 0.307
[40, 300] loss: 0.300
[40, 360] loss: 0.318
Epoch: 40 -> Loss: 0.338285326958
Epoch: 40 -> Test Accuracy: 80.83
[41, 60] loss: 0.285
[41, 120] loss: 0.265
[41, 180] loss: 0.272
[41, 240] loss: 0.279
[41, 300] loss: 0.261
[41, 360] loss: 0.263
Epoch: 41 -> Loss: 0.30100017786
Epoch: 41 -> Test Accuracy: 82.36
[42, 60] loss: 0.257
[42, 120] loss: 0.243
[42, 180] loss: 0.264
[42, 240] loss: 0.252
[42, 300] loss: 0.263
[42, 360] loss: 0.270
Epoch: 42 -> Loss: 0.420700728893
Epoch: 42 -> Test Accuracy: 82.38
[43, 60] loss: 0.258
[43, 120] loss: 0.241
[43, 180] loss: 0.249
[43, 240] loss: 0.242
[43, 300] loss: 0.249
[43, 360] loss: 0.252
Epoch: 43 -> Loss: 0.241235524416
Epoch: 43 -> Test Accuracy: 82.18
[44, 60] loss: 0.234
[44, 120] loss: 0.237
[44, 180] loss: 0.228
[44, 240] loss: 0.245
[44, 300] loss: 0.247
[44, 360] loss: 0.241
Epoch: 44 -> Loss: 0.193565994501
Epoch: 44 -> Test Accuracy: 82.62
[45, 60] loss: 0.223
[45, 120] loss: 0.223
[45, 180] loss: 0.228
[45, 240] loss: 0.244
[45, 300] loss: 0.236
[45, 360] loss: 0.237
Epoch: 45 -> Loss: 0.336680471897
Epoch: 45 -> Test Accuracy: 82.62
[46, 60] loss: 0.216
[46, 120] loss: 0.220
[46, 180] loss: 0.224
[46, 240] loss: 0.227
[46, 300] loss: 0.220
[46, 360] loss: 0.231
Epoch: 46 -> Loss: 0.201660081744
Epoch: 46 -> Test Accuracy: 82.63
[47, 60] loss: 0.217
[47, 120] loss: 0.214
[47, 180] loss: 0.219
[47, 240] loss: 0.222
[47, 300] loss: 0.216
[47, 360] loss: 0.237
Epoch: 47 -> Loss: 0.283549129963
Epoch: 47 -> Test Accuracy: 82.51
[48, 60] loss: 0.217
[48, 120] loss: 0.216
[48, 180] loss: 0.226
[48, 240] loss: 0.229
[48, 300] loss: 0.224
[48, 360] loss: 0.225
Epoch: 48 -> Loss: 0.179385825992
Epoch: 48 -> Test Accuracy: 82.48
[49, 60] loss: 0.222
[49, 120] loss: 0.213
[49, 180] loss: 0.208
[49, 240] loss: 0.219
[49, 300] loss: 0.230
[49, 360] loss: 0.206
Epoch: 49 -> Loss: 0.187668353319
Epoch: 49 -> Test Accuracy: 82.6
[50, 60] loss: 0.232
[50, 120] loss: 0.208
[50, 180] loss: 0.230
[50, 240] loss: 0.224
[50, 300] loss: 0.217
[50, 360] loss: 0.223
Epoch: 50 -> Loss: 0.270434826612
Epoch: 50 -> Test Accuracy: 82.56
[51, 60] loss: 0.231
[51, 120] loss: 0.204
[51, 180] loss: 0.205
[51, 240] loss: 0.228
[51, 300] loss: 0.225
[51, 360] loss: 0.211
Epoch: 51 -> Loss: 0.11189135164
Epoch: 51 -> Test Accuracy: 82.51
[52, 60] loss: 0.223
[52, 120] loss: 0.215
[52, 180] loss: 0.203
[52, 240] loss: 0.211
[52, 300] loss: 0.220
[52, 360] loss: 0.220
Epoch: 52 -> Loss: 0.250686466694
Epoch: 52 -> Test Accuracy: 82.51
[53, 60] loss: 0.198
[53, 120] loss: 0.217
[53, 180] loss: 0.198
[53, 240] loss: 0.213
[53, 300] loss: 0.215
[53, 360] loss: 0.222
Epoch: 53 -> Loss: 0.163546591997
Epoch: 53 -> Test Accuracy: 82.46
[54, 60] loss: 0.211
[54, 120] loss: 0.201
[54, 180] loss: 0.218
[54, 240] loss: 0.211
[54, 300] loss: 0.215
[54, 360] loss: 0.216
Epoch: 54 -> Loss: 0.26966124773
Epoch: 54 -> Test Accuracy: 82.57
[55, 60] loss: 0.207
[55, 120] loss: 0.225
[55, 180] loss: 0.210
[55, 240] loss: 0.214
[55, 300] loss: 0.217
[55, 360] loss: 0.218
Epoch: 55 -> Loss: 0.195465743542
Epoch: 55 -> Test Accuracy: 82.62
[56, 60] loss: 0.213
[56, 120] loss: 0.208
[56, 180] loss: 0.203
[56, 240] loss: 0.224
[56, 300] loss: 0.202
[56, 360] loss: 0.219
Epoch: 56 -> Loss: 0.161485761404
Epoch: 56 -> Test Accuracy: 82.74
[57, 60] loss: 0.204
[57, 120] loss: 0.207
[57, 180] loss: 0.216
[57, 240] loss: 0.207
[57, 300] loss: 0.209
[57, 360] loss: 0.206
Epoch: 57 -> Loss: 0.172239303589
Epoch: 57 -> Test Accuracy: 82.7
[58, 60] loss: 0.206
[58, 120] loss: 0.206
[58, 180] loss: 0.204
[58, 240] loss: 0.209
[58, 300] loss: 0.205
[58, 360] loss: 0.212
Epoch: 58 -> Loss: 0.146751895547
Epoch: 58 -> Test Accuracy: 82.69
[59, 60] loss: 0.204
[59, 120] loss: 0.205
[59, 180] loss: 0.197
[59, 240] loss: 0.210
[59, 300] loss: 0.201
[59, 360] loss: 0.203
Epoch: 59 -> Loss: 0.207809656858
Epoch: 59 -> Test Accuracy: 82.52
[60, 60] loss: 0.210
[60, 120] loss: 0.200
[60, 180] loss: 0.202
[60, 240] loss: 0.207
[60, 300] loss: 0.216
[60, 360] loss: 0.215
Epoch: 60 -> Loss: 0.289859235287
Epoch: 60 -> Test Accuracy: 82.5
[61, 60] loss: 0.210
[61, 120] loss: 0.203
[61, 180] loss: 0.197
[61, 240] loss: 0.198
[61, 300] loss: 0.209
[61, 360] loss: 0.196
Epoch: 61 -> Loss: 0.305670619011
Epoch: 61 -> Test Accuracy: 82.65
[62, 60] loss: 0.200
[62, 120] loss: 0.212
[62, 180] loss: 0.213
[62, 240] loss: 0.202
[62, 300] loss: 0.206
[62, 360] loss: 0.217
Epoch: 62 -> Loss: 0.172732159495
Epoch: 62 -> Test Accuracy: 82.54
[63, 60] loss: 0.198
[63, 120] loss: 0.199
[63, 180] loss: 0.198
[63, 240] loss: 0.208
[63, 300] loss: 0.197
[63, 360] loss: 0.214
Epoch: 63 -> Loss: 0.234139487147
Epoch: 63 -> Test Accuracy: 82.6
[64, 60] loss: 0.194
[64, 120] loss: 0.205
[64, 180] loss: 0.196
[64, 240] loss: 0.206
[64, 300] loss: 0.208
[64, 360] loss: 0.197
Epoch: 64 -> Loss: 0.276698976755
Epoch: 64 -> Test Accuracy: 82.62
[65, 60] loss: 0.202
[65, 120] loss: 0.209
[65, 180] loss: 0.192
[65, 240] loss: 0.191
[65, 300] loss: 0.201
[65, 360] loss: 0.210
Epoch: 65 -> Loss: 0.181875705719
Epoch: 65 -> Test Accuracy: 82.52
[66, 60] loss: 0.191
[66, 120] loss: 0.192
[66, 180] loss: 0.197
[66, 240] loss: 0.200
[66, 300] loss: 0.197
[66, 360] loss: 0.190
Epoch: 66 -> Loss: 0.210397556424
Epoch: 66 -> Test Accuracy: 82.66
[67, 60] loss: 0.190
[67, 120] loss: 0.198
[67, 180] loss: 0.195
[67, 240] loss: 0.203
[67, 300] loss: 0.205
[67, 360] loss: 0.202
Epoch: 67 -> Loss: 0.103144481778
Epoch: 67 -> Test Accuracy: 82.47
[68, 60] loss: 0.191
[68, 120] loss: 0.199
[68, 180] loss: 0.200
[68, 240] loss: 0.196
[68, 300] loss: 0.195
[68, 360] loss: 0.192
Epoch: 68 -> Loss: 0.177246764302
Epoch: 68 -> Test Accuracy: 82.53
[69, 60] loss: 0.194
[69, 120] loss: 0.186
[69, 180] loss: 0.186
[69, 240] loss: 0.202
[69, 300] loss: 0.193
[69, 360] loss: 0.199
Epoch: 69 -> Loss: 0.328603744507
Epoch: 69 -> Test Accuracy: 82.66
[70, 60] loss: 0.188
[70, 120] loss: 0.186
[70, 180] loss: 0.191
[70, 240] loss: 0.193
[70, 300] loss: 0.186
[70, 360] loss: 0.193
Epoch: 70 -> Loss: 0.1943808496
Epoch: 70 -> Test Accuracy: 82.77
[71, 60] loss: 0.196
[71, 120] loss: 0.201
[71, 180] loss: 0.192
[71, 240] loss: 0.193
[71, 300] loss: 0.196
[71, 360] loss: 0.202
Epoch: 71 -> Loss: 0.20616979897
Epoch: 71 -> Test Accuracy: 82.75
[72, 60] loss: 0.201
[72, 120] loss: 0.190
[72, 180] loss: 0.191
[72, 240] loss: 0.194
[72, 300] loss: 0.197
[72, 360] loss: 0.206
Epoch: 72 -> Loss: 0.27528283
Epoch: 72 -> Test Accuracy: 82.67
[73, 60] loss: 0.192
[73, 120] loss: 0.189
[73, 180] loss: 0.192
[73, 240] loss: 0.188
[73, 300] loss: 0.192
[73, 360] loss: 0.186
Epoch: 73 -> Loss: 0.182364612818
Epoch: 73 -> Test Accuracy: 82.62
[74, 60] loss: 0.186
[74, 120] loss: 0.191
[74, 180] loss: 0.188
[74, 240] loss: 0.196
[74, 300] loss: 0.195
[74, 360] loss: 0.189
Epoch: 74 -> Loss: 0.21389195323
Epoch: 74 -> Test Accuracy: 82.76
[75, 60] loss: 0.179
[75, 120] loss: 0.196
[75, 180] loss: 0.191
[75, 240] loss: 0.190
[75, 300] loss: 0.190
[75, 360] loss: 0.196
Epoch: 75 -> Loss: 0.149008527398
Epoch: 75 -> Test Accuracy: 82.75
[76, 60] loss: 0.192
[76, 120] loss: 0.183
[76, 180] loss: 0.197
[76, 240] loss: 0.193
[76, 300] loss: 0.190
[76, 360] loss: 0.189
Epoch: 76 -> Loss: 0.234543561935
Epoch: 76 -> Test Accuracy: 82.56
[77, 60] loss: 0.191
[77, 120] loss: 0.194
[77, 180] loss: 0.181
[77, 240] loss: 0.188
[77, 300] loss: 0.192
[77, 360] loss: 0.183
Epoch: 77 -> Loss: 0.160980522633
Epoch: 77 -> Test Accuracy: 82.49
[78, 60] loss: 0.182
[78, 120] loss: 0.198
[78, 180] loss: 0.183
[78, 240] loss: 0.185
[78, 300] loss: 0.180
[78, 360] loss: 0.184
Epoch: 78 -> Loss: 0.154733732343
Epoch: 78 -> Test Accuracy: 82.64
[79, 60] loss: 0.195
[79, 120] loss: 0.177
[79, 180] loss: 0.188
[79, 240] loss: 0.183
[79, 300] loss: 0.184
[79, 360] loss: 0.203
Epoch: 79 -> Loss: 0.251921266317
Epoch: 79 -> Test Accuracy: 82.6
[80, 60] loss: 0.193
[80, 120] loss: 0.183
[80, 180] loss: 0.174
[80, 240] loss: 0.176
[80, 300] loss: 0.185
[80, 360] loss: 0.190
Epoch: 80 -> Loss: 0.18039470911
Epoch: 80 -> Test Accuracy: 82.61
[81, 60] loss: 0.187
[81, 120] loss: 0.182
[81, 180] loss: 0.181
[81, 240] loss: 0.184
[81, 300] loss: 0.190
[81, 360] loss: 0.197
Epoch: 81 -> Loss: 0.156178563833
Epoch: 81 -> Test Accuracy: 82.62
[82, 60] loss: 0.192
[82, 120] loss: 0.181
[82, 180] loss: 0.177
[82, 240] loss: 0.197
[82, 300] loss: 0.178
[82, 360] loss: 0.182
Epoch: 82 -> Loss: 0.164409220219
Epoch: 82 -> Test Accuracy: 82.59
[83, 60] loss: 0.189
[83, 120] loss: 0.177
[83, 180] loss: 0.176
[83, 240] loss: 0.191
[83, 300] loss: 0.189
[83, 360] loss: 0.183
Epoch: 83 -> Loss: 0.374662697315
Epoch: 83 -> Test Accuracy: 82.76
[84, 60] loss: 0.171
[84, 120] loss: 0.184
[84, 180] loss: 0.190
[84, 240] loss: 0.195
[84, 300] loss: 0.186
[84, 360] loss: 0.183
Epoch: 84 -> Loss: 0.261164724827
Epoch: 84 -> Test Accuracy: 82.69
[85, 60] loss: 0.186
[85, 120] loss: 0.182
[85, 180] loss: 0.188
[85, 240] loss: 0.176
[85, 300] loss: 0.193
[85, 360] loss: 0.189
Epoch: 85 -> Loss: 0.308419048786
Epoch: 85 -> Test Accuracy: 82.6
[86, 60] loss: 0.177
[86, 120] loss: 0.178
[86, 180] loss: 0.180
[86, 240] loss: 0.186
[86, 300] loss: 0.182
[86, 360] loss: 0.190
Epoch: 86 -> Loss: 0.183074206114
Epoch: 86 -> Test Accuracy: 82.72
[87, 60] loss: 0.178
[87, 120] loss: 0.186
[87, 180] loss: 0.188
[87, 240] loss: 0.172
[87, 300] loss: 0.174
[87, 360] loss: 0.184
Epoch: 87 -> Loss: 0.148081868887
Epoch: 87 -> Test Accuracy: 82.61
[88, 60] loss: 0.174
[88, 120] loss: 0.190
[88, 180] loss: 0.190
[88, 240] loss: 0.171
[88, 300] loss: 0.179
[88, 360] loss: 0.185
Epoch: 88 -> Loss: 0.237703084946
Epoch: 88 -> Test Accuracy: 82.58
[89, 60] loss: 0.185
[89, 120] loss: 0.177
[89, 180] loss: 0.174
[89, 240] loss: 0.181
[89, 300] loss: 0.175
[89, 360] loss: 0.180
Epoch: 89 -> Loss: 0.174320682883
Epoch: 89 -> Test Accuracy: 82.74
[90, 60] loss: 0.177
[90, 120] loss: 0.180
[90, 180] loss: 0.172
[90, 240] loss: 0.192
[90, 300] loss: 0.170
[90, 360] loss: 0.171
Epoch: 90 -> Loss: 0.0973794907331
Epoch: 90 -> Test Accuracy: 82.68
[91, 60] loss: 0.169
[91, 120] loss: 0.178
[91, 180] loss: 0.175
[91, 240] loss: 0.176
[91, 300] loss: 0.175
[91, 360] loss: 0.181
Epoch: 91 -> Loss: 0.243659883738
Epoch: 91 -> Test Accuracy: 82.55
[92, 60] loss: 0.178
[92, 120] loss: 0.180
[92, 180] loss: 0.173
[92, 240] loss: 0.173
[92, 300] loss: 0.192
[92, 360] loss: 0.170
Epoch: 92 -> Loss: 0.156590640545
Epoch: 92 -> Test Accuracy: 82.67
[93, 60] loss: 0.170
[93, 120] loss: 0.176
[93, 180] loss: 0.177
[93, 240] loss: 0.161
[93, 300] loss: 0.176
[93, 360] loss: 0.179
Epoch: 93 -> Loss: 0.119468547404
Epoch: 93 -> Test Accuracy: 82.52
[94, 60] loss: 0.164
[94, 120] loss: 0.170
[94, 180] loss: 0.175
[94, 240] loss: 0.175
[94, 300] loss: 0.173
[94, 360] loss: 0.176
Epoch: 94 -> Loss: 0.200928539038
Epoch: 94 -> Test Accuracy: 82.57
[95, 60] loss: 0.175
[95, 120] loss: 0.186
[95, 180] loss: 0.168
[95, 240] loss: 0.172
[95, 300] loss: 0.170
[95, 360] loss: 0.177
Epoch: 95 -> Loss: 0.22417716682
Epoch: 95 -> Test Accuracy: 82.67
[96, 60] loss: 0.175
[96, 120] loss: 0.175
[96, 180] loss: 0.173
[96, 240] loss: 0.167
[96, 300] loss: 0.170
[96, 360] loss: 0.171
Epoch: 96 -> Loss: 0.214863657951
Epoch: 96 -> Test Accuracy: 82.53
[97, 60] loss: 0.174
[97, 120] loss: 0.169
[97, 180] loss: 0.184
[97, 240] loss: 0.171
[97, 300] loss: 0.173
[97, 360] loss: 0.176
Epoch: 97 -> Loss: 0.113119915128
Epoch: 97 -> Test Accuracy: 82.4
[98, 60] loss: 0.182
[98, 120] loss: 0.174
[98, 180] loss: 0.170
[98, 240] loss: 0.169
[98, 300] loss: 0.178
[98, 360] loss: 0.182
Epoch: 98 -> Loss: 0.194167688489
Epoch: 98 -> Test Accuracy: 82.58
[99, 60] loss: 0.175
[99, 120] loss: 0.179
[99, 180] loss: 0.171
[99, 240] loss: 0.174
[99, 300] loss: 0.177
[99, 360] loss: 0.163
Epoch: 99 -> Loss: 0.12676396966
Epoch: 99 -> Test Accuracy: 82.54
[100, 60] loss: 0.174
[100, 120] loss: 0.169
[100, 180] loss: 0.174
[100, 240] loss: 0.175
[100, 300] loss: 0.170
[100, 360] loss: 0.163
Epoch: 100 -> Loss: 0.092672303319
Epoch: 100 -> Test Accuracy: 82.54
Finished Training
[1, 60] loss: 2.818
[1, 120] loss: 2.012
[1, 180] loss: 1.942
[1, 240] loss: 1.912
[1, 300] loss: 1.868
[1, 360] loss: 1.851
Epoch: 1 -> Loss: 1.65035951138
Epoch: 1 -> Test Accuracy: 30.72
[2, 60] loss: 1.816
[2, 120] loss: 1.791
[2, 180] loss: 1.794
[2, 240] loss: 1.777
[2, 300] loss: 1.781
[2, 360] loss: 1.757
Epoch: 2 -> Loss: 1.74936366081
Epoch: 2 -> Test Accuracy: 33.34
[3, 60] loss: 1.732
[3, 120] loss: 1.734
[3, 180] loss: 1.727
[3, 240] loss: 1.717
[3, 300] loss: 1.724
[3, 360] loss: 1.714
Epoch: 3 -> Loss: 1.56685900688
Epoch: 3 -> Test Accuracy: 35.05
[4, 60] loss: 1.716
[4, 120] loss: 1.682
[4, 180] loss: 1.696
[4, 240] loss: 1.702
[4, 300] loss: 1.701
[4, 360] loss: 1.699
Epoch: 4 -> Loss: 1.88525998592
Epoch: 4 -> Test Accuracy: 34.81
[5, 60] loss: 1.681
[5, 120] loss: 1.680
[5, 180] loss: 1.707
[5, 240] loss: 1.669
[5, 300] loss: 1.692
[5, 360] loss: 1.683
Epoch: 5 -> Loss: 1.73990762234
Epoch: 5 -> Test Accuracy: 35.49
[6, 60] loss: 1.693
[6, 120] loss: 1.676
[6, 180] loss: 1.670
[6, 240] loss: 1.651
[6, 300] loss: 1.648
[6, 360] loss: 1.670
Epoch: 6 -> Loss: 1.51242232323
Epoch: 6 -> Test Accuracy: 35.59
[7, 60] loss: 1.670
[7, 120] loss: 1.664
[7, 180] loss: 1.655
[7, 240] loss: 1.673
[7, 300] loss: 1.660
[7, 360] loss: 1.656
Epoch: 7 -> Loss: 1.63683533669
Epoch: 7 -> Test Accuracy: 35.55
[8, 60] loss: 1.668
[8, 120] loss: 1.656
[8, 180] loss: 1.650
[8, 240] loss: 1.645
[8, 300] loss: 1.662
[8, 360] loss: 1.640
Epoch: 8 -> Loss: 1.55725729465
Epoch: 8 -> Test Accuracy: 35.92
[9, 60] loss: 1.659
[9, 120] loss: 1.668
[9, 180] loss: 1.640
[9, 240] loss: 1.662
[9, 300] loss: 1.640
[9, 360] loss: 1.653
Epoch: 9 -> Loss: 1.57050871849
Epoch: 9 -> Test Accuracy: 36.05
[10, 60] loss: 1.657
[10, 120] loss: 1.640
[10, 180] loss: 1.647
[10, 240] loss: 1.635
[10, 300] loss: 1.662
[10, 360] loss: 1.644
Epoch: 10 -> Loss: 1.65176618099
Epoch: 10 -> Test Accuracy: 37.38
[11, 60] loss: 1.630
[11, 120] loss: 1.632
[11, 180] loss: 1.644
[11, 240] loss: 1.635
[11, 300] loss: 1.667
[11, 360] loss: 1.619
Epoch: 11 -> Loss: 1.63378334045
Epoch: 11 -> Test Accuracy: 37.26
[12, 60] loss: 1.618
[12, 120] loss: 1.642
[12, 180] loss: 1.622
[12, 240] loss: 1.633
[12, 300] loss: 1.631
[12, 360] loss: 1.650
Epoch: 12 -> Loss: 1.75447976589
Epoch: 12 -> Test Accuracy: 37.32
[13, 60] loss: 1.634
[13, 120] loss: 1.631
[13, 180] loss: 1.637
[13, 240] loss: 1.650
[13, 300] loss: 1.639
[13, 360] loss: 1.622
Epoch: 13 -> Loss: 1.68028259277
Epoch: 13 -> Test Accuracy: 37.32
[14, 60] loss: 1.633
[14, 120] loss: 1.626
[14, 180] loss: 1.619
[14, 240] loss: 1.638
[14, 300] loss: 1.637
[14, 360] loss: 1.651
Epoch: 14 -> Loss: 1.66220152378
Epoch: 14 -> Test Accuracy: 36.28
[15, 60] loss: 1.635
[15, 120] loss: 1.627
[15, 180] loss: 1.626
[15, 240] loss: 1.622
[15, 300] loss: 1.629
[15, 360] loss: 1.659
Epoch: 15 -> Loss: 1.72832965851
Epoch: 15 -> Test Accuracy: 36.58
[16, 60] loss: 1.629
[16, 120] loss: 1.630
[16, 180] loss: 1.630
[16, 240] loss: 1.630
[16, 300] loss: 1.605
[16, 360] loss: 1.620
Epoch: 16 -> Loss: 1.54632163048
Epoch: 16 -> Test Accuracy: 37.75
[17, 60] loss: 1.619
[17, 120] loss: 1.625
[17, 180] loss: 1.629
[17, 240] loss: 1.634
[17, 300] loss: 1.643
[17, 360] loss: 1.622
Epoch: 17 -> Loss: 1.61425685883
Epoch: 17 -> Test Accuracy: 37.55
[18, 60] loss: 1.618
[18, 120] loss: 1.614
[18, 180] loss: 1.623
[18, 240] loss: 1.615
[18, 300] loss: 1.617
[18, 360] loss: 1.623
Epoch: 18 -> Loss: 1.82452487946
Epoch: 18 -> Test Accuracy: 37.74
[19, 60] loss: 1.627
[19, 120] loss: 1.623
[19, 180] loss: 1.620
[19, 240] loss: 1.627
[19, 300] loss: 1.625
[19, 360] loss: 1.615
Epoch: 19 -> Loss: 1.74086797237
Epoch: 19 -> Test Accuracy: 36.92
[20, 60] loss: 1.618
[20, 120] loss: 1.610
[20, 180] loss: 1.611
[20, 240] loss: 1.616
[20, 300] loss: 1.619
[20, 360] loss: 1.615
Epoch: 20 -> Loss: 1.73075580597
Epoch: 20 -> Test Accuracy: 36.39
[21, 60] loss: 1.576
[21, 120] loss: 1.561
[21, 180] loss: 1.534
[21, 240] loss: 1.530
[21, 300] loss: 1.518
[21, 360] loss: 1.525
Epoch: 21 -> Loss: 1.47958314419
Epoch: 21 -> Test Accuracy: 39.53
[22, 60] loss: 1.511
[22, 120] loss: 1.497
[22, 180] loss: 1.529
[22, 240] loss: 1.516
[22, 300] loss: 1.509
[22, 360] loss: 1.509
Epoch: 22 -> Loss: 1.46137690544
Epoch: 22 -> Test Accuracy: 40.37
[23, 60] loss: 1.486
[23, 120] loss: 1.496
[23, 180] loss: 1.522
[23, 240] loss: 1.486
[23, 300] loss: 1.494
[23, 360] loss: 1.477
Epoch: 23 -> Loss: 1.57786989212
Epoch: 23 -> Test Accuracy: 40.34
[24, 60] loss: 1.483
[24, 120] loss: 1.489
[24, 180] loss: 1.478
[24, 240] loss: 1.505
[24, 300] loss: 1.477
[24, 360] loss: 1.494
Epoch: 24 -> Loss: 1.42657911777
Epoch: 24 -> Test Accuracy: 40.61
[25, 60] loss: 1.485
[25, 120] loss: 1.474
[25, 180] loss: 1.490
[25, 240] loss: 1.490
[25, 300] loss: 1.484
[25, 360] loss: 1.489
Epoch: 25 -> Loss: 1.53933930397
Epoch: 25 -> Test Accuracy: 40.45
[26, 60] loss: 1.469
[26, 120] loss: 1.485
[26, 180] loss: 1.472
[26, 240] loss: 1.484
[26, 300] loss: 1.477
[26, 360] loss: 1.487
Epoch: 26 -> Loss: 1.41461062431
Epoch: 26 -> Test Accuracy: 41.02
[27, 60] loss: 1.473
[27, 120] loss: 1.487
[27, 180] loss: 1.482
[27, 240] loss: 1.486
[27, 300] loss: 1.471
[27, 360] loss: 1.498
Epoch: 27 -> Loss: 1.315107584
Epoch: 27 -> Test Accuracy: 41.59
[28, 60] loss: 1.483
[28, 120] loss: 1.469
[28, 180] loss: 1.485
[28, 240] loss: 1.473
[28, 300] loss: 1.488
[28, 360] loss: 1.476
Epoch: 28 -> Loss: 1.40484452248
Epoch: 28 -> Test Accuracy: 41.09
[29, 60] loss: 1.481
[29, 120] loss: 1.469
[29, 180] loss: 1.485
[29, 240] loss: 1.476
[29, 300] loss: 1.470
[29, 360] loss: 1.465
Epoch: 29 -> Loss: 1.45765554905
Epoch: 29 -> Test Accuracy: 41.56
[30, 60] loss: 1.470
[30, 120] loss: 1.472
[30, 180] loss: 1.475
[30, 240] loss: 1.482
[30, 300] loss: 1.483
[30, 360] loss: 1.484
Epoch: 30 -> Loss: 1.46095252037
Epoch: 30 -> Test Accuracy: 41.19
[31, 60] loss: 1.471
[31, 120] loss: 1.462
[31, 180] loss: 1.484
[31, 240] loss: 1.467
[31, 300] loss: 1.474
[31, 360] loss: 1.462
Epoch: 31 -> Loss: 1.70025658607
Epoch: 31 -> Test Accuracy: 40.76
[32, 60] loss: 1.483
[32, 120] loss: 1.468
[32, 180] loss: 1.479
[32, 240] loss: 1.471
[32, 300] loss: 1.484
[32, 360] loss: 1.463
Epoch: 32 -> Loss: 1.44530463219
Epoch: 32 -> Test Accuracy: 41.73
[33, 60] loss: 1.458
[33, 120] loss: 1.446
[33, 180] loss: 1.482
[33, 240] loss: 1.481
[33, 300] loss: 1.483
[33, 360] loss: 1.470
Epoch: 33 -> Loss: 1.26609611511
Epoch: 33 -> Test Accuracy: 41.65
[34, 60] loss: 1.469
[34, 120] loss: 1.482
[34, 180] loss: 1.477
[34, 240] loss: 1.482
[34, 300] loss: 1.470
[34, 360] loss: 1.467
Epoch: 34 -> Loss: 1.47667622566
Epoch: 34 -> Test Accuracy: 41.26
[35, 60] loss: 1.469
[35, 120] loss: 1.471
[35, 180] loss: 1.482
[35, 240] loss: 1.487
[35, 300] loss: 1.472
[35, 360] loss: 1.470
Epoch: 35 -> Loss: 1.43761456013
Epoch: 35 -> Test Accuracy: 41.89
[36, 60] loss: 1.444
[36, 120] loss: 1.472
[36, 180] loss: 1.485
[36, 240] loss: 1.449
[36, 300] loss: 1.483
[36, 360] loss: 1.474
Epoch: 36 -> Loss: 1.41081655025
Epoch: 36 -> Test Accuracy: 41.5
[37, 60] loss: 1.456
[37, 120] loss: 1.457
[37, 180] loss: 1.454
[37, 240] loss: 1.480
[37, 300] loss: 1.475
[37, 360] loss: 1.473
Epoch: 37 -> Loss: 1.52845048904
Epoch: 37 -> Test Accuracy: 41.66
[38, 60] loss: 1.476
[38, 120] loss: 1.462
[38, 180] loss: 1.455
[38, 240] loss: 1.469
[38, 300] loss: 1.474
[38, 360] loss: 1.466
Epoch: 38 -> Loss: 1.4974886179
Epoch: 38 -> Test Accuracy: 41.39
[39, 60] loss: 1.481
[39, 120] loss: 1.476
[39, 180] loss: 1.477
[39, 240] loss: 1.454
[39, 300] loss: 1.461
[39, 360] loss: 1.460
Epoch: 39 -> Loss: 1.44558370113
Epoch: 39 -> Test Accuracy: 41.45
[40, 60] loss: 1.446
[40, 120] loss: 1.489
[40, 180] loss: 1.469
[40, 240] loss: 1.489
[40, 300] loss: 1.458
[40, 360] loss: 1.477
Epoch: 40 -> Loss: 1.40977978706
Epoch: 40 -> Test Accuracy: 41.07
[41, 60] loss: 1.449
[41, 120] loss: 1.434
[41, 180] loss: 1.425
[41, 240] loss: 1.417
[41, 300] loss: 1.394
[41, 360] loss: 1.410
Epoch: 41 -> Loss: 1.45218515396
Epoch: 41 -> Test Accuracy: 43.15
[42, 60] loss: 1.403
[42, 120] loss: 1.414
[42, 180] loss: 1.391
[42, 240] loss: 1.378
[42, 300] loss: 1.397
[42, 360] loss: 1.415
Epoch: 42 -> Loss: 1.45976185799
Epoch: 42 -> Test Accuracy: 43.57
[43, 60] loss: 1.410
[43, 120] loss: 1.389
[43, 180] loss: 1.407
[43, 240] loss: 1.369
[43, 300] loss: 1.384
[43, 360] loss: 1.405
Epoch: 43 -> Loss: 1.63363111019
Epoch: 43 -> Test Accuracy: 43.79
[44, 60] loss: 1.381
[44, 120] loss: 1.387
[44, 180] loss: 1.398
[44, 240] loss: 1.375
[44, 300] loss: 1.376
[44, 360] loss: 1.389
Epoch: 44 -> Loss: 1.39831256866
Epoch: 44 -> Test Accuracy: 44.2
[45, 60] loss: 1.369
[45, 120] loss: 1.387
[45, 180] loss: 1.391
[45, 240] loss: 1.374
[45, 300] loss: 1.381
[45, 360] loss: 1.390
Epoch: 45 -> Loss: 1.3232729435
Epoch: 45 -> Test Accuracy: 44.02
[46, 60] loss: 1.393
[46, 120] loss: 1.376
[46, 180] loss: 1.354
[46, 240] loss: 1.355
[46, 300] loss: 1.358
[46, 360] loss: 1.381
Epoch: 46 -> Loss: 1.37403297424
Epoch: 46 -> Test Accuracy: 44.28
[47, 60] loss: 1.339
[47, 120] loss: 1.371
[47, 180] loss: 1.383
[47, 240] loss: 1.374
[47, 300] loss: 1.355
[47, 360] loss: 1.366
Epoch: 47 -> Loss: 1.34893035889
Epoch: 47 -> Test Accuracy: 44.27
[48, 60] loss: 1.362
[48, 120] loss: 1.363
[48, 180] loss: 1.346
[48, 240] loss: 1.358
[48, 300] loss: 1.377
[48, 360] loss: 1.357
Epoch: 48 -> Loss: 1.38947319984
Epoch: 48 -> Test Accuracy: 44.51
[49, 60] loss: 1.352
[49, 120] loss: 1.351
[49, 180] loss: 1.360
[49, 240] loss: 1.366
[49, 300] loss: 1.353
[49, 360] loss: 1.356
Epoch: 49 -> Loss: 1.32876336575
Epoch: 49 -> Test Accuracy: 44.48
[50, 60] loss: 1.367
[50, 120] loss: 1.354
[50, 180] loss: 1.347
[50, 240] loss: 1.356
[50, 300] loss: 1.348
[50, 360] loss: 1.357
Epoch: 50 -> Loss: 1.3062171936
Epoch: 50 -> Test Accuracy: 44.77
[51, 60] loss: 1.327
[51, 120] loss: 1.362
[51, 180] loss: 1.375
[51, 240] loss: 1.357
[51, 300] loss: 1.330
[51, 360] loss: 1.359
Epoch: 51 -> Loss: 1.44059967995
Epoch: 51 -> Test Accuracy: 44.71
[52, 60] loss: 1.357
[52, 120] loss: 1.360
[52, 180] loss: 1.362
[52, 240] loss: 1.354
[52, 300] loss: 1.346
[52, 360] loss: 1.337
Epoch: 52 -> Loss: 1.40836787224
Epoch: 52 -> Test Accuracy: 44.7
[53, 60] loss: 1.354
[53, 120] loss: 1.364
[53, 180] loss: 1.354
[53, 240] loss: 1.359
[53, 300] loss: 1.354
[53, 360] loss: 1.357
Epoch: 53 -> Loss: 1.20832812786
Epoch: 53 -> Test Accuracy: 44.63
[54, 60] loss: 1.341
[54, 120] loss: 1.363
[54, 180] loss: 1.367
[54, 240] loss: 1.335
[54, 300] loss: 1.376
[54, 360] loss: 1.353
Epoch: 54 -> Loss: 1.49905264378
Epoch: 54 -> Test Accuracy: 44.6
[55, 60] loss: 1.362
[55, 120] loss: 1.350
[55, 180] loss: 1.344
[55, 240] loss: 1.346
[55, 300] loss: 1.338
[55, 360] loss: 1.343
Epoch: 55 -> Loss: 1.25955271721
Epoch: 55 -> Test Accuracy: 44.48
[56, 60] loss: 1.336
[56, 120] loss: 1.350
[56, 180] loss: 1.351
[56, 240] loss: 1.334
[56, 300] loss: 1.364
[56, 360] loss: 1.343
Epoch: 56 -> Loss: 1.45857298374
Epoch: 56 -> Test Accuracy: 44.86
[57, 60] loss: 1.354
[57, 120] loss: 1.326
[57, 180] loss: 1.362
[57, 240] loss: 1.328
[57, 300] loss: 1.353
[57, 360] loss: 1.348
Epoch: 57 -> Loss: 1.294267416
Epoch: 57 -> Test Accuracy: 44.36
[58, 60] loss: 1.353
[58, 120] loss: 1.363
[58, 180] loss: 1.342
[58, 240] loss: 1.341
[58, 300] loss: 1.338
[58, 360] loss: 1.343
Epoch: 58 -> Loss: 1.40837979317
Epoch: 58 -> Test Accuracy: 44.59
[59, 60] loss: 1.326
[59, 120] loss: 1.362
[59, 180] loss: 1.368
[59, 240] loss: 1.354
[59, 300] loss: 1.335
[59, 360] loss: 1.337
Epoch: 59 -> Loss: 1.46184694767
Epoch: 59 -> Test Accuracy: 44.7
[60, 60] loss: 1.327
[60, 120] loss: 1.338
[60, 180] loss: 1.325
[60, 240] loss: 1.345
[60, 300] loss: 1.353
[60, 360] loss: 1.333
Epoch: 60 -> Loss: 1.27309501171
Epoch: 60 -> Test Accuracy: 44.68
[61, 60] loss: 1.345
[61, 120] loss: 1.354
[61, 180] loss: 1.333
[61, 240] loss: 1.336
[61, 300] loss: 1.343
[61, 360] loss: 1.350
Epoch: 61 -> Loss: 1.40847659111
Epoch: 61 -> Test Accuracy: 44.65
[62, 60] loss: 1.334
[62, 120] loss: 1.338
[62, 180] loss: 1.338
[62, 240] loss: 1.340
[62, 300] loss: 1.336
[62, 360] loss: 1.347
Epoch: 62 -> Loss: 1.25489187241
Epoch: 62 -> Test Accuracy: 44.45
[63, 60] loss: 1.331
[63, 120] loss: 1.334
[63, 180] loss: 1.355
[63, 240] loss: 1.337
[63, 300] loss: 1.338
[63, 360] loss: 1.357
Epoch: 63 -> Loss: 1.25219511986
Epoch: 63 -> Test Accuracy: 44.55
[64, 60] loss: 1.345
[64, 120] loss: 1.359
[64, 180] loss: 1.329
[64, 240] loss: 1.332
[64, 300] loss: 1.338
[64, 360] loss: 1.354
Epoch: 64 -> Loss: 1.47248375416
Epoch: 64 -> Test Accuracy: 44.68
[65, 60] loss: 1.342
[65, 120] loss: 1.332
[65, 180] loss: 1.337
[65, 240] loss: 1.340
[65, 300] loss: 1.349
[65, 360] loss: 1.336
Epoch: 65 -> Loss: 1.36289978027
Epoch: 65 -> Test Accuracy: 44.87
[66, 60] loss: 1.353
[66, 120] loss: 1.336
[66, 180] loss: 1.343
[66, 240] loss: 1.337
[66, 300] loss: 1.324
[66, 360] loss: 1.348
Epoch: 66 -> Loss: 1.60350251198
Epoch: 66 -> Test Accuracy: 44.7
[67, 60] loss: 1.330
[67, 120] loss: 1.325
[67, 180] loss: 1.337
[67, 240] loss: 1.351
[67, 300] loss: 1.349
[67, 360] loss: 1.338
Epoch: 67 -> Loss: 1.08013629913
Epoch: 67 -> Test Accuracy: 44.8
[68, 60] loss: 1.349
[68, 120] loss: 1.338
[68, 180] loss: 1.330
[68, 240] loss: 1.338
[68, 300] loss: 1.358
[68, 360] loss: 1.352
Epoch: 68 -> Loss: 1.21484231949
Epoch: 68 -> Test Accuracy: 44.84
[69, 60] loss: 1.334
[69, 120] loss: 1.323
[69, 180] loss: 1.327
[69, 240] loss: 1.343
[69, 300] loss: 1.362
[69, 360] loss: 1.336
Epoch: 69 -> Loss: 1.54353761673
Epoch: 69 -> Test Accuracy: 44.87
[70, 60] loss: 1.346
[70, 120] loss: 1.333
[70, 180] loss: 1.340
[70, 240] loss: 1.329
[70, 300] loss: 1.348
[70, 360] loss: 1.338
Epoch: 70 -> Loss: 1.63164269924
Epoch: 70 -> Test Accuracy: 44.96
[71, 60] loss: 1.327
[71, 120] loss: 1.321
[71, 180] loss: 1.351
[71, 240] loss: 1.343
[71, 300] loss: 1.327
[71, 360] loss: 1.335
Epoch: 71 -> Loss: 1.33832609653
Epoch: 71 -> Test Accuracy: 45.0
[72, 60] loss: 1.335
[72, 120] loss: 1.326
[72, 180] loss: 1.350
[72, 240] loss: 1.336
[72, 300] loss: 1.348
[72, 360] loss: 1.349
Epoch: 72 -> Loss: 1.33676230907
Epoch: 72 -> Test Accuracy: 44.95
[73, 60] loss: 1.319
[73, 120] loss: 1.336
[73, 180] loss: 1.323
[73, 240] loss: 1.340
[73, 300] loss: 1.354
[73, 360] loss: 1.338
Epoch: 73 -> Loss: 1.20308446884
Epoch: 73 -> Test Accuracy: 45.18
[74, 60] loss: 1.348
[74, 120] loss: 1.337
[74, 180] loss: 1.334
[74, 240] loss: 1.338
[74, 300] loss: 1.319
[74, 360] loss: 1.336
Epoch: 74 -> Loss: 1.53289163113
Epoch: 74 -> Test Accuracy: 45.07
[75, 60] loss: 1.324
[75, 120] loss: 1.339
[75, 180] loss: 1.311
[75, 240] loss: 1.335
[75, 300] loss: 1.335
[75, 360] loss: 1.336
Epoch: 75 -> Loss: 1.37990307808
Epoch: 75 -> Test Accuracy: 45.04
[76, 60] loss: 1.342
[76, 120] loss: 1.320
[76, 180] loss: 1.315
[76, 240] loss: 1.357
[76, 300] loss: 1.336
[76, 360] loss: 1.339
Epoch: 76 -> Loss: 1.41355466843
Epoch: 76 -> Test Accuracy: 45.14
[77, 60] loss: 1.324
[77, 120] loss: 1.345
[77, 180] loss: 1.351
[77, 240] loss: 1.342
[77, 300] loss: 1.324
[77, 360] loss: 1.348
Epoch: 77 -> Loss: 1.48257124424
Epoch: 77 -> Test Accuracy: 45.17
[78, 60] loss: 1.323
[78, 120] loss: 1.323
[78, 180] loss: 1.324
[78, 240] loss: 1.344
[78, 300] loss: 1.335
[78, 360] loss: 1.316
Epoch: 78 -> Loss: 1.22074568272
Epoch: 78 -> Test Accuracy: 45.05
[79, 60] loss: 1.334
[79, 120] loss: 1.333
[79, 180] loss: 1.317
[79, 240] loss: 1.337
[79, 300] loss: 1.329
[79, 360] loss: 1.327
Epoch: 79 -> Loss: 1.44437217712
Epoch: 79 -> Test Accuracy: 45.29
[80, 60] loss: 1.316
[80, 120] loss: 1.326
[80, 180] loss: 1.327
[80, 240] loss: 1.321
[80, 300] loss: 1.320
[80, 360] loss: 1.338
Epoch: 80 -> Loss: 1.48346507549
Epoch: 80 -> Test Accuracy: 45.06
[81, 60] loss: 1.320
[81, 120] loss: 1.331
[81, 180] loss: 1.337
[81, 240] loss: 1.328
[81, 300] loss: 1.341
[81, 360] loss: 1.341
Epoch: 81 -> Loss: 1.38029301167
Epoch: 81 -> Test Accuracy: 45.24
[82, 60] loss: 1.314
[82, 120] loss: 1.349
[82, 180] loss: 1.312
[82, 240] loss: 1.326
[82, 300] loss: 1.327
[82, 360] loss: 1.338
Epoch: 82 -> Loss: 1.34826076031
Epoch: 82 -> Test Accuracy: 45.06
[83, 60] loss: 1.334
[83, 120] loss: 1.324
[83, 180] loss: 1.322
[83, 240] loss: 1.319
[83, 300] loss: 1.339
[83, 360] loss: 1.329
Epoch: 83 -> Loss: 1.21827435493
Epoch: 83 -> Test Accuracy: 45.21
[84, 60] loss: 1.321
[84, 120] loss: 1.328
[84, 180] loss: 1.354
[84, 240] loss: 1.327
[84, 300] loss: 1.334
[84, 360] loss: 1.314
Epoch: 84 -> Loss: 1.53800415993
Epoch: 84 -> Test Accuracy: 45.16
[85, 60] loss: 1.304
[85, 120] loss: 1.347
[85, 180] loss: 1.329
[85, 240] loss: 1.346
[85, 300] loss: 1.316
[85, 360] loss: 1.320
Epoch: 85 -> Loss: 1.34503436089
Epoch: 85 -> Test Accuracy: 45.05
[86, 60] loss: 1.318
[86, 120] loss: 1.345
[86, 180] loss: 1.312
[86, 240] loss: 1.334
[86, 300] loss: 1.336
[86, 360] loss: 1.325
Epoch: 86 -> Loss: 1.40680634975
Epoch: 86 -> Test Accuracy: 44.88
[87, 60] loss: 1.318
[87, 120] loss: 1.325
[87, 180] loss: 1.330
[87, 240] loss: 1.317
[87, 300] loss: 1.333
[87, 360] loss: 1.335
Epoch: 87 -> Loss: 1.33049619198
Epoch: 87 -> Test Accuracy: 45.14
[88, 60] loss: 1.315
[88, 120] loss: 1.311
[88, 180] loss: 1.316
[88, 240] loss: 1.325
[88, 300] loss: 1.340
[88, 360] loss: 1.331
Epoch: 88 -> Loss: 1.29452824593
Epoch: 88 -> Test Accuracy: 45.17
[89, 60] loss: 1.313
[89, 120] loss: 1.321
[89, 180] loss: 1.332
[89, 240] loss: 1.310
[89, 300] loss: 1.322
[89, 360] loss: 1.340
Epoch: 89 -> Loss: 1.57597136497
Epoch: 89 -> Test Accuracy: 44.82
[90, 60] loss: 1.317
[90, 120] loss: 1.322
[90, 180] loss: 1.337
[90, 240] loss: 1.314
[90, 300] loss: 1.324
[90, 360] loss: 1.331
Epoch: 90 -> Loss: 1.14589643478
Epoch: 90 -> Test Accuracy: 44.7
[91, 60] loss: 1.328
[91, 120] loss: 1.324
[91, 180] loss: 1.322
[91, 240] loss: 1.333
[91, 300] loss: 1.318
[91, 360] loss: 1.328
Epoch: 91 -> Loss: 1.24610877037
Epoch: 91 -> Test Accuracy: 45.18
[92, 60] loss: 1.322
[92, 120] loss: 1.321
[92, 180] loss: 1.321
[92, 240] loss: 1.334
[92, 300] loss: 1.307
[92, 360] loss: 1.316
Epoch: 92 -> Loss: 1.27793669701
Epoch: 92 -> Test Accuracy: 44.78
[93, 60] loss: 1.318
[93, 120] loss: 1.353
[93, 180] loss: 1.320
[93, 240] loss: 1.315
[93, 300] loss: 1.333
[93, 360] loss: 1.341
Epoch: 93 -> Loss: 1.31564640999
Epoch: 93 -> Test Accuracy: 45.0
[94, 60] loss: 1.305
[94, 120] loss: 1.308
[94, 180] loss: 1.306
[94, 240] loss: 1.343
[94, 300] loss: 1.336
[94, 360] loss: 1.320
Epoch: 94 -> Loss: 1.31199002266
Epoch: 94 -> Test Accuracy: 44.77
[95, 60] loss: 1.313
[95, 120] loss: 1.334
[95, 180] loss: 1.318
[95, 240] loss: 1.318
[95, 300] loss: 1.301
[95, 360] loss: 1.330
Epoch: 95 -> Loss: 1.41564643383
Epoch: 95 -> Test Accuracy: 44.84
[96, 60] loss: 1.318
[96, 120] loss: 1.314
[96, 180] loss: 1.329
[96, 240] loss: 1.326
[96, 300] loss: 1.322
[96, 360] loss: 1.315
Epoch: 96 -> Loss: 1.38825726509
Epoch: 96 -> Test Accuracy: 44.99
[97, 60] loss: 1.323
[97, 120] loss: 1.315
[97, 180] loss: 1.320
[97, 240] loss: 1.345
[97, 300] loss: 1.323
[97, 360] loss: 1.318
Epoch: 97 -> Loss: 1.39025008678
Epoch: 97 -> Test Accuracy: 44.8
[98, 60] loss: 1.313
[98, 120] loss: 1.302
[98, 180] loss: 1.326
[98, 240] loss: 1.338
[98, 300] loss: 1.325
[98, 360] loss: 1.321
Epoch: 98 -> Loss: 1.34853088856
Epoch: 98 -> Test Accuracy: 45.01
[99, 60] loss: 1.305
[99, 120] loss: 1.324
[99, 180] loss: 1.320
[99, 240] loss: 1.313
[99, 300] loss: 1.330
[99, 360] loss: 1.321
Epoch: 99 -> Loss: 1.39544785023
Epoch: 99 -> Test Accuracy: 45.17
[100, 60] loss: 1.313
[100, 120] loss: 1.312
[100, 180] loss: 1.313
[100, 240] loss: 1.315
[100, 300] loss: 1.314
[100, 360] loss: 1.305
Epoch: 100 -> Loss: 1.22449326515
Epoch: 100 -> Test Accuracy: 45.3
Finished Training
In [9]:
# train ConvClassifiers on feature map of net_3block
conv_block4_loss_log, _, conv_block4_test_accuracy_log, _, _ = tr.train_all_blocks(4, 10, [0.1, 0.02, 0.004, 0.0008], 
    [35, 70, 85, 100], 0.9, 5e-4, net_block4, criterion, trainloader, None, testloader, use_ConvClassifier=True) 
[1, 60] loss: 1.372
[1, 120] loss: 1.074
[1, 180] loss: 0.946
[1, 240] loss: 0.909
[1, 300] loss: 0.884
[1, 360] loss: 0.838
Epoch: 1 -> Loss: 0.927259802818
Epoch: 1 -> Test Accuracy: 69.98
[2, 60] loss: 0.768
[2, 120] loss: 0.743
[2, 180] loss: 0.742
[2, 240] loss: 0.712
[2, 300] loss: 0.679
[2, 360] loss: 0.691
Epoch: 2 -> Loss: 0.617124915123
Epoch: 2 -> Test Accuracy: 74.04
[3, 60] loss: 0.652
[3, 120] loss: 0.655
[3, 180] loss: 0.645
[3, 240] loss: 0.647
[3, 300] loss: 0.624
[3, 360] loss: 0.621
Epoch: 3 -> Loss: 0.637347102165
Epoch: 3 -> Test Accuracy: 75.37
[4, 60] loss: 0.605
[4, 120] loss: 0.604
[4, 180] loss: 0.613
[4, 240] loss: 0.590
[4, 300] loss: 0.598
[4, 360] loss: 0.564
Epoch: 4 -> Loss: 0.387685835361
Epoch: 4 -> Test Accuracy: 76.34
[5, 60] loss: 0.557
[5, 120] loss: 0.569
[5, 180] loss: 0.550
[5, 240] loss: 0.555
[5, 300] loss: 0.573
[5, 360] loss: 0.573
Epoch: 5 -> Loss: 0.502129793167
Epoch: 5 -> Test Accuracy: 77.77
[6, 60] loss: 0.534
[6, 120] loss: 0.560
[6, 180] loss: 0.539
[6, 240] loss: 0.549
[6, 300] loss: 0.557
[6, 360] loss: 0.542
Epoch: 6 -> Loss: 0.361016571522
Epoch: 6 -> Test Accuracy: 78.28
[7, 60] loss: 0.539
[7, 120] loss: 0.514
[7, 180] loss: 0.537
[7, 240] loss: 0.529
[7, 300] loss: 0.514
[7, 360] loss: 0.538
Epoch: 7 -> Loss: 0.482561647892
Epoch: 7 -> Test Accuracy: 80.08
[8, 60] loss: 0.500
[8, 120] loss: 0.508
[8, 180] loss: 0.522
[8, 240] loss: 0.510
[8, 300] loss: 0.513
[8, 360] loss: 0.503
Epoch: 8 -> Loss: 0.55847465992
Epoch: 8 -> Test Accuracy: 80.35
[9, 60] loss: 0.480
[9, 120] loss: 0.501
[9, 180] loss: 0.497
[9, 240] loss: 0.501
[9, 300] loss: 0.522
[9, 360] loss: 0.518
Epoch: 9 -> Loss: 0.620239019394
Epoch: 9 -> Test Accuracy: 78.19
[10, 60] loss: 0.481
[10, 120] loss: 0.483
[10, 180] loss: 0.501
[10, 240] loss: 0.474
[10, 300] loss: 0.522
[10, 360] loss: 0.480
Epoch: 10 -> Loss: 0.398448884487
Epoch: 10 -> Test Accuracy: 79.7
[11, 60] loss: 0.488
[11, 120] loss: 0.488
[11, 180] loss: 0.479
[11, 240] loss: 0.488
[11, 300] loss: 0.497
[11, 360] loss: 0.489
Epoch: 11 -> Loss: 0.534637212753
Epoch: 11 -> Test Accuracy: 79.93
[12, 60] loss: 0.458
[12, 120] loss: 0.482
[12, 180] loss: 0.483
[12, 240] loss: 0.492
[12, 300] loss: 0.490
[12, 360] loss: 0.466
Epoch: 12 -> Loss: 0.386262238026
Epoch: 12 -> Test Accuracy: 80.51
[13, 60] loss: 0.434
[13, 120] loss: 0.467
[13, 180] loss: 0.458
[13, 240] loss: 0.472
[13, 300] loss: 0.493
[13, 360] loss: 0.497
Epoch: 13 -> Loss: 0.476347744465
Epoch: 13 -> Test Accuracy: 80.91
[14, 60] loss: 0.463
[14, 120] loss: 0.468
[14, 180] loss: 0.470
[14, 240] loss: 0.457
[14, 300] loss: 0.485
[14, 360] loss: 0.478
Epoch: 14 -> Loss: 0.516170620918
Epoch: 14 -> Test Accuracy: 80.41
[15, 60] loss: 0.459
[15, 120] loss: 0.452
[15, 180] loss: 0.461
[15, 240] loss: 0.465
[15, 300] loss: 0.464
[15, 360] loss: 0.487
Epoch: 15 -> Loss: 0.485541522503
Epoch: 15 -> Test Accuracy: 80.19
[16, 60] loss: 0.437
[16, 120] loss: 0.453
[16, 180] loss: 0.462
[16, 240] loss: 0.446
[16, 300] loss: 0.461
[16, 360] loss: 0.472
Epoch: 16 -> Loss: 0.498241096735
Epoch: 16 -> Test Accuracy: 80.32
[17, 60] loss: 0.437
[17, 120] loss: 0.427
[17, 180] loss: 0.472
[17, 240] loss: 0.462
[17, 300] loss: 0.460
[17, 360] loss: 0.471
Epoch: 17 -> Loss: 0.313609927893
Epoch: 17 -> Test Accuracy: 81.16
[18, 60] loss: 0.426
[18, 120] loss: 0.450
[18, 180] loss: 0.453
[18, 240] loss: 0.439
[18, 300] loss: 0.448
[18, 360] loss: 0.463
Epoch: 18 -> Loss: 0.437183380127
Epoch: 18 -> Test Accuracy: 81.39
[19, 60] loss: 0.436
[19, 120] loss: 0.452
[19, 180] loss: 0.450
[19, 240] loss: 0.438
[19, 300] loss: 0.461
[19, 360] loss: 0.452
Epoch: 19 -> Loss: 0.398821175098
Epoch: 19 -> Test Accuracy: 79.81
[20, 60] loss: 0.413
[20, 120] loss: 0.445
[20, 180] loss: 0.463
[20, 240] loss: 0.444
[20, 300] loss: 0.453
[20, 360] loss: 0.466
Epoch: 20 -> Loss: 0.49925032258
Epoch: 20 -> Test Accuracy: 79.81
[21, 60] loss: 0.415
[21, 120] loss: 0.427
[21, 180] loss: 0.439
[21, 240] loss: 0.468
[21, 300] loss: 0.454
[21, 360] loss: 0.472
Epoch: 21 -> Loss: 0.444497525692
Epoch: 21 -> Test Accuracy: 79.35
[22, 60] loss: 0.444
[22, 120] loss: 0.433
[22, 180] loss: 0.444
[22, 240] loss: 0.440
[22, 300] loss: 0.456
[22, 360] loss: 0.445
Epoch: 22 -> Loss: 0.283289670944
Epoch: 22 -> Test Accuracy: 80.5
[23, 60] loss: 0.403
[23, 120] loss: 0.427
[23, 180] loss: 0.452
[23, 240] loss: 0.448
[23, 300] loss: 0.450
[23, 360] loss: 0.450
Epoch: 23 -> Loss: 0.413048088551
Epoch: 23 -> Test Accuracy: 81.67
[24, 60] loss: 0.412
[24, 120] loss: 0.433
[24, 180] loss: 0.444
[24, 240] loss: 0.451
[24, 300] loss: 0.444
[24, 360] loss: 0.444
Epoch: 24 -> Loss: 0.227589562535
Epoch: 24 -> Test Accuracy: 81.36
[25, 60] loss: 0.420
[25, 120] loss: 0.437
[25, 180] loss: 0.449
[25, 240] loss: 0.428
[25, 300] loss: 0.453
[25, 360] loss: 0.442
Epoch: 25 -> Loss: 0.322181284428
Epoch: 25 -> Test Accuracy: 79.74
[26, 60] loss: 0.418
[26, 120] loss: 0.414
[26, 180] loss: 0.427
[26, 240] loss: 0.432
[26, 300] loss: 0.453
[26, 360] loss: 0.469
Epoch: 26 -> Loss: 0.476414352655
Epoch: 26 -> Test Accuracy: 80.22
[27, 60] loss: 0.411
[27, 120] loss: 0.422
[27, 180] loss: 0.429
[27, 240] loss: 0.439
[27, 300] loss: 0.448
[27, 360] loss: 0.436
Epoch: 27 -> Loss: 0.452017396688
Epoch: 27 -> Test Accuracy: 81.22
[28, 60] loss: 0.418
[28, 120] loss: 0.423
[28, 180] loss: 0.434
[28, 240] loss: 0.442
[28, 300] loss: 0.426
[28, 360] loss: 0.448
Epoch: 28 -> Loss: 0.456640064716
Epoch: 28 -> Test Accuracy: 81.29
[29, 60] loss: 0.402
[29, 120] loss: 0.421
[29, 180] loss: 0.441
[29, 240] loss: 0.442
[29, 300] loss: 0.449
[29, 360] loss: 0.451
Epoch: 29 -> Loss: 0.445857822895
Epoch: 29 -> Test Accuracy: 81.41
[30, 60] loss: 0.407
[30, 120] loss: 0.412
[30, 180] loss: 0.440
[30, 240] loss: 0.427
[30, 300] loss: 0.432
[30, 360] loss: 0.442
Epoch: 30 -> Loss: 0.485147058964
Epoch: 30 -> Test Accuracy: 80.7
[31, 60] loss: 0.410
[31, 120] loss: 0.425
[31, 180] loss: 0.422
[31, 240] loss: 0.427
[31, 300] loss: 0.426
[31, 360] loss: 0.452
Epoch: 31 -> Loss: 0.38826367259
Epoch: 31 -> Test Accuracy: 80.35
[32, 60] loss: 0.389
[32, 120] loss: 0.441
[32, 180] loss: 0.433
[32, 240] loss: 0.431
[32, 300] loss: 0.453
[32, 360] loss: 0.449
Epoch: 32 -> Loss: 0.395451396704
Epoch: 32 -> Test Accuracy: 81.43
[33, 60] loss: 0.402
[33, 120] loss: 0.422
[33, 180] loss: 0.441
[33, 240] loss: 0.442
[33, 300] loss: 0.435
[33, 360] loss: 0.428
Epoch: 33 -> Loss: 0.417356312275
Epoch: 33 -> Test Accuracy: 80.4
[34, 60] loss: 0.420
[34, 120] loss: 0.404
[34, 180] loss: 0.436
[34, 240] loss: 0.427
[34, 300] loss: 0.448
[34, 360] loss: 0.429
Epoch: 34 -> Loss: 0.32553473115
Epoch: 34 -> Test Accuracy: 79.84
[35, 60] loss: 0.410
[35, 120] loss: 0.412
[35, 180] loss: 0.413
[35, 240] loss: 0.443
[35, 300] loss: 0.438
[35, 360] loss: 0.447
Epoch: 35 -> Loss: 0.587553262711
Epoch: 35 -> Test Accuracy: 81.9
[36, 60] loss: 0.352
[36, 120] loss: 0.297
[36, 180] loss: 0.295
[36, 240] loss: 0.289
[36, 300] loss: 0.285
[36, 360] loss: 0.304
Epoch: 36 -> Loss: 0.188715487719
Epoch: 36 -> Test Accuracy: 85.13
[37, 60] loss: 0.270
[37, 120] loss: 0.279
[37, 180] loss: 0.266
[37, 240] loss: 0.274
[37, 300] loss: 0.269
[37, 360] loss: 0.256
Epoch: 37 -> Loss: 0.32158690691
Epoch: 37 -> Test Accuracy: 85.65
[38, 60] loss: 0.260
[38, 120] loss: 0.252
[38, 180] loss: 0.249
[38, 240] loss: 0.255
[38, 300] loss: 0.259
[38, 360] loss: 0.261
Epoch: 38 -> Loss: 0.306841343641
Epoch: 38 -> Test Accuracy: 84.64
[39, 60] loss: 0.235
[39, 120] loss: 0.259
[39, 180] loss: 0.241
[39, 240] loss: 0.245
[39, 300] loss: 0.255
[39, 360] loss: 0.255
Epoch: 39 -> Loss: 0.322778463364
Epoch: 39 -> Test Accuracy: 84.96
[40, 60] loss: 0.241
[40, 120] loss: 0.237
[40, 180] loss: 0.232
[40, 240] loss: 0.251
[40, 300] loss: 0.256
[40, 360] loss: 0.255
Epoch: 40 -> Loss: 0.195762485266
Epoch: 40 -> Test Accuracy: 85.03
[41, 60] loss: 0.225
[41, 120] loss: 0.240
[41, 180] loss: 0.241
[41, 240] loss: 0.235
[41, 300] loss: 0.256
[41, 360] loss: 0.243
Epoch: 41 -> Loss: 0.306314736605
Epoch: 41 -> Test Accuracy: 85.12
[42, 60] loss: 0.223
[42, 120] loss: 0.236
[42, 180] loss: 0.229
[42, 240] loss: 0.243
[42, 300] loss: 0.248
[42, 360] loss: 0.248
Epoch: 42 -> Loss: 0.209075495601
Epoch: 42 -> Test Accuracy: 84.72
[43, 60] loss: 0.228
[43, 120] loss: 0.228
[43, 180] loss: 0.220
[43, 240] loss: 0.245
[43, 300] loss: 0.242
[43, 360] loss: 0.252
Epoch: 43 -> Loss: 0.350984156132
Epoch: 43 -> Test Accuracy: 84.75
[44, 60] loss: 0.224
[44, 120] loss: 0.240
[44, 180] loss: 0.227
[44, 240] loss: 0.233
[44, 300] loss: 0.247
[44, 360] loss: 0.256
Epoch: 44 -> Loss: 0.243943408132
Epoch: 44 -> Test Accuracy: 85.28
[45, 60] loss: 0.230
[45, 120] loss: 0.229
[45, 180] loss: 0.231
[45, 240] loss: 0.235
[45, 300] loss: 0.243
[45, 360] loss: 0.252
Epoch: 45 -> Loss: 0.29595375061
Epoch: 45 -> Test Accuracy: 85.05
[46, 60] loss: 0.223
[46, 120] loss: 0.242
[46, 180] loss: 0.226
[46, 240] loss: 0.235
[46, 300] loss: 0.246
[46, 360] loss: 0.239
Epoch: 46 -> Loss: 0.233119890094
Epoch: 46 -> Test Accuracy: 84.48
[47, 60] loss: 0.214
[47, 120] loss: 0.230
[47, 180] loss: 0.236
[47, 240] loss: 0.254
[47, 300] loss: 0.236
[47, 360] loss: 0.244
Epoch: 47 -> Loss: 0.138906747103
Epoch: 47 -> Test Accuracy: 84.91
[48, 60] loss: 0.229
[48, 120] loss: 0.224
[48, 180] loss: 0.235
[48, 240] loss: 0.235
[48, 300] loss: 0.257
[48, 360] loss: 0.248
Epoch: 48 -> Loss: 0.282572805882
Epoch: 48 -> Test Accuracy: 84.55
[49, 60] loss: 0.232
[49, 120] loss: 0.236
[49, 180] loss: 0.245
[49, 240] loss: 0.231
[49, 300] loss: 0.229
[49, 360] loss: 0.249
Epoch: 49 -> Loss: 0.206251949072
Epoch: 49 -> Test Accuracy: 83.8
[50, 60] loss: 0.226
[50, 120] loss: 0.228
[50, 180] loss: 0.243
[50, 240] loss: 0.243
[50, 300] loss: 0.243
[50, 360] loss: 0.242
Epoch: 50 -> Loss: 0.20590980351
Epoch: 50 -> Test Accuracy: 84.09
[51, 60] loss: 0.220
[51, 120] loss: 0.233
[51, 180] loss: 0.225
[51, 240] loss: 0.243
[51, 300] loss: 0.237
[51, 360] loss: 0.255
Epoch: 51 -> Loss: 0.193142607808
Epoch: 51 -> Test Accuracy: 85.22
[52, 60] loss: 0.218
[52, 120] loss: 0.219
[52, 180] loss: 0.232
[52, 240] loss: 0.238
[52, 300] loss: 0.240
[52, 360] loss: 0.258
Epoch: 52 -> Loss: 0.203998044133
Epoch: 52 -> Test Accuracy: 84.16
[53, 60] loss: 0.222
[53, 120] loss: 0.239
[53, 180] loss: 0.225
[53, 240] loss: 0.232
[53, 300] loss: 0.251
[53, 360] loss: 0.237
Epoch: 53 -> Loss: 0.213248178363
Epoch: 53 -> Test Accuracy: 85.02
[54, 60] loss: 0.216
[54, 120] loss: 0.225
[54, 180] loss: 0.229
[54, 240] loss: 0.243
[54, 300] loss: 0.242
[54, 360] loss: 0.251
Epoch: 54 -> Loss: 0.288084477186
Epoch: 54 -> Test Accuracy: 84.64
[55, 60] loss: 0.215
[55, 120] loss: 0.218
[55, 180] loss: 0.232
[55, 240] loss: 0.251
[55, 300] loss: 0.239
[55, 360] loss: 0.234
Epoch: 55 -> Loss: 0.217760756612
Epoch: 55 -> Test Accuracy: 84.0
[56, 60] loss: 0.226
[56, 120] loss: 0.222
[56, 180] loss: 0.235
[56, 240] loss: 0.238
[56, 300] loss: 0.238
[56, 360] loss: 0.257
Epoch: 56 -> Loss: 0.300151914358
Epoch: 56 -> Test Accuracy: 84.23
[57, 60] loss: 0.224
[57, 120] loss: 0.233
[57, 180] loss: 0.236
[57, 240] loss: 0.227
[57, 300] loss: 0.243
[57, 360] loss: 0.235
Epoch: 57 -> Loss: 0.274967849255
Epoch: 57 -> Test Accuracy: 84.37
[58, 60] loss: 0.216
[58, 120] loss: 0.219
[58, 180] loss: 0.230
[58, 240] loss: 0.242
[58, 300] loss: 0.247
[58, 360] loss: 0.239
Epoch: 58 -> Loss: 0.264767020941
Epoch: 58 -> Test Accuracy: 84.05
[59, 60] loss: 0.235
[59, 120] loss: 0.225
[59, 180] loss: 0.217
[59, 240] loss: 0.227
[59, 300] loss: 0.235
[59, 360] loss: 0.249
Epoch: 59 -> Loss: 0.28347197175
Epoch: 59 -> Test Accuracy: 84.67
[60, 60] loss: 0.221
[60, 120] loss: 0.231
[60, 180] loss: 0.234
[60, 240] loss: 0.230
[60, 300] loss: 0.228
[60, 360] loss: 0.249
Epoch: 60 -> Loss: 0.229270026088
Epoch: 60 -> Test Accuracy: 83.87
[61, 60] loss: 0.219
[61, 120] loss: 0.223
[61, 180] loss: 0.237
[61, 240] loss: 0.231
[61, 300] loss: 0.237
[61, 360] loss: 0.258
Epoch: 61 -> Loss: 0.197796553373
Epoch: 61 -> Test Accuracy: 84.29
[62, 60] loss: 0.209
[62, 120] loss: 0.221
[62, 180] loss: 0.241
[62, 240] loss: 0.230
[62, 300] loss: 0.233
[62, 360] loss: 0.237
Epoch: 62 -> Loss: 0.221377894282
Epoch: 62 -> Test Accuracy: 84.52
[63, 60] loss: 0.204
[63, 120] loss: 0.220
[63, 180] loss: 0.234
[63, 240] loss: 0.240
[63, 300] loss: 0.236
[63, 360] loss: 0.249
Epoch: 63 -> Loss: 0.273113131523
Epoch: 63 -> Test Accuracy: 83.77
[64, 60] loss: 0.221
[64, 120] loss: 0.221
[64, 180] loss: 0.213
[64, 240] loss: 0.240
[64, 300] loss: 0.238
[64, 360] loss: 0.242
Epoch: 64 -> Loss: 0.244653537869
Epoch: 64 -> Test Accuracy: 84.29
[65, 60] loss: 0.218
[65, 120] loss: 0.221
[65, 180] loss: 0.233
[65, 240] loss: 0.225
[65, 300] loss: 0.231
[65, 360] loss: 0.226
Epoch: 65 -> Loss: 0.354703366756
Epoch: 65 -> Test Accuracy: 84.65
[66, 60] loss: 0.213
[66, 120] loss: 0.214
[66, 180] loss: 0.228
[66, 240] loss: 0.222
[66, 300] loss: 0.231
[66, 360] loss: 0.231
Epoch: 66 -> Loss: 0.25256896019
Epoch: 66 -> Test Accuracy: 84.73
[67, 60] loss: 0.213
[67, 120] loss: 0.214
[67, 180] loss: 0.224
[67, 240] loss: 0.231
[67, 300] loss: 0.224
[67, 360] loss: 0.233
Epoch: 67 -> Loss: 0.305030316114
Epoch: 67 -> Test Accuracy: 83.48
[68, 60] loss: 0.225
[68, 120] loss: 0.214
[68, 180] loss: 0.220
[68, 240] loss: 0.227
[68, 300] loss: 0.232
[68, 360] loss: 0.240
Epoch: 68 -> Loss: 0.171532616019
Epoch: 68 -> Test Accuracy: 84.13
[69, 60] loss: 0.218
[69, 120] loss: 0.226
[69, 180] loss: 0.227
[69, 240] loss: 0.216
[69, 300] loss: 0.229
[69, 360] loss: 0.239
Epoch: 69 -> Loss: 0.43925729394
Epoch: 69 -> Test Accuracy: 85.02
[70, 60] loss: 0.218
[70, 120] loss: 0.219
[70, 180] loss: 0.222
[70, 240] loss: 0.225
[70, 300] loss: 0.227
[70, 360] loss: 0.234
Epoch: 70 -> Loss: 0.268076896667
Epoch: 70 -> Test Accuracy: 83.92
[71, 60] loss: 0.182
[71, 120] loss: 0.163
[71, 180] loss: 0.154
[71, 240] loss: 0.160
[71, 300] loss: 0.154
[71, 360] loss: 0.148
Epoch: 71 -> Loss: 0.134753674269
Epoch: 71 -> Test Accuracy: 86.3
[72, 60] loss: 0.137
[72, 120] loss: 0.137
[72, 180] loss: 0.139
[72, 240] loss: 0.141
[72, 300] loss: 0.142
[72, 360] loss: 0.140
Epoch: 72 -> Loss: 0.126617431641
Epoch: 72 -> Test Accuracy: 86.51
[73, 60] loss: 0.131
[73, 120] loss: 0.132
[73, 180] loss: 0.133
[73, 240] loss: 0.128
[73, 300] loss: 0.136
[73, 360] loss: 0.129
Epoch: 73 -> Loss: 0.219107463956
Epoch: 73 -> Test Accuracy: 86.78
[74, 60] loss: 0.124
[74, 120] loss: 0.133
[74, 180] loss: 0.125
[74, 240] loss: 0.128
[74, 300] loss: 0.131
[74, 360] loss: 0.131
Epoch: 74 -> Loss: 0.130234628916
Epoch: 74 -> Test Accuracy: 86.51
[75, 60] loss: 0.120
[75, 120] loss: 0.123
[75, 180] loss: 0.124
[75, 240] loss: 0.125
[75, 300] loss: 0.126
[75, 360] loss: 0.123
Epoch: 75 -> Loss: 0.15600335598
Epoch: 75 -> Test Accuracy: 86.38
[76, 60] loss: 0.113
[76, 120] loss: 0.120
[76, 180] loss: 0.118
[76, 240] loss: 0.130
[76, 300] loss: 0.125
[76, 360] loss: 0.121
Epoch: 76 -> Loss: 0.161401793361
Epoch: 76 -> Test Accuracy: 86.8
[77, 60] loss: 0.116
[77, 120] loss: 0.113
[77, 180] loss: 0.116
[77, 240] loss: 0.120
[77, 300] loss: 0.131
[77, 360] loss: 0.116
Epoch: 77 -> Loss: 0.1019628793
Epoch: 77 -> Test Accuracy: 86.52
[78, 60] loss: 0.109
[78, 120] loss: 0.118
[78, 180] loss: 0.119
[78, 240] loss: 0.114
[78, 300] loss: 0.119
[78, 360] loss: 0.125
Epoch: 78 -> Loss: 0.163000136614
Epoch: 78 -> Test Accuracy: 86.54
[79, 60] loss: 0.114
[79, 120] loss: 0.109
[79, 180] loss: 0.120
[79, 240] loss: 0.120
[79, 300] loss: 0.117
[79, 360] loss: 0.116
Epoch: 79 -> Loss: 0.172471135855
Epoch: 79 -> Test Accuracy: 86.19
[80, 60] loss: 0.106
[80, 120] loss: 0.111
[80, 180] loss: 0.109
[80, 240] loss: 0.117
[80, 300] loss: 0.122
[80, 360] loss: 0.121
Epoch: 80 -> Loss: 0.145307347178
Epoch: 80 -> Test Accuracy: 85.97
[81, 60] loss: 0.108
[81, 120] loss: 0.105
[81, 180] loss: 0.107
[81, 240] loss: 0.119
[81, 300] loss: 0.109
[81, 360] loss: 0.120
Epoch: 81 -> Loss: 0.0871049165726
Epoch: 81 -> Test Accuracy: 86.18
[82, 60] loss: 0.096
[82, 120] loss: 0.114
[82, 180] loss: 0.108
[82, 240] loss: 0.115
[82, 300] loss: 0.110
[82, 360] loss: 0.115
Epoch: 82 -> Loss: 0.169233441353
Epoch: 82 -> Test Accuracy: 86.2
[83, 60] loss: 0.102
[83, 120] loss: 0.107
[83, 180] loss: 0.111
[83, 240] loss: 0.102
[83, 300] loss: 0.105
[83, 360] loss: 0.112
Epoch: 83 -> Loss: 0.085696414113
Epoch: 83 -> Test Accuracy: 86.61
[84, 60] loss: 0.102
[84, 120] loss: 0.107
[84, 180] loss: 0.109
[84, 240] loss: 0.112
[84, 300] loss: 0.105
[84, 360] loss: 0.109
Epoch: 84 -> Loss: 0.0708574280143
Epoch: 84 -> Test Accuracy: 86.27
[85, 60] loss: 0.105
[85, 120] loss: 0.100
[85, 180] loss: 0.100
[85, 240] loss: 0.106
[85, 300] loss: 0.114
[85, 360] loss: 0.104
Epoch: 85 -> Loss: 0.132660195231
Epoch: 85 -> Test Accuracy: 86.39
[86, 60] loss: 0.101
[86, 120] loss: 0.095
[86, 180] loss: 0.094
[86, 240] loss: 0.096
[86, 300] loss: 0.091
[86, 360] loss: 0.093
Epoch: 86 -> Loss: 0.175499588251
Epoch: 86 -> Test Accuracy: 86.56
[87, 60] loss: 0.088
[87, 120] loss: 0.096
[87, 180] loss: 0.090
[87, 240] loss: 0.089
[87, 300] loss: 0.093
[87, 360] loss: 0.090
Epoch: 87 -> Loss: 0.0678234323859
Epoch: 87 -> Test Accuracy: 86.6
[88, 60] loss: 0.088
[88, 120] loss: 0.090
[88, 180] loss: 0.097
[88, 240] loss: 0.093
[88, 300] loss: 0.093
[88, 360] loss: 0.088
Epoch: 88 -> Loss: 0.100074604154
Epoch: 88 -> Test Accuracy: 86.68
[89, 60] loss: 0.088
[89, 120] loss: 0.092
[89, 180] loss: 0.091
[89, 240] loss: 0.087
[89, 300] loss: 0.086
[89, 360] loss: 0.087
Epoch: 89 -> Loss: 0.0597632043064
Epoch: 89 -> Test Accuracy: 86.54
[90, 60] loss: 0.088
[90, 120] loss: 0.087
[90, 180] loss: 0.094
[90, 240] loss: 0.092
[90, 300] loss: 0.089
[90, 360] loss: 0.092
Epoch: 90 -> Loss: 0.102328419685
Epoch: 90 -> Test Accuracy: 86.37
[91, 60] loss: 0.082
[91, 120] loss: 0.083
[91, 180] loss: 0.088
[91, 240] loss: 0.088
[91, 300] loss: 0.095
[91, 360] loss: 0.085
Epoch: 91 -> Loss: 0.105287432671
Epoch: 91 -> Test Accuracy: 86.46
[92, 60] loss: 0.084
[92, 120] loss: 0.089
[92, 180] loss: 0.079
[92, 240] loss: 0.087
[92, 300] loss: 0.091
[92, 360] loss: 0.089
Epoch: 92 -> Loss: 0.0610167384148
Epoch: 92 -> Test Accuracy: 86.46
[93, 60] loss: 0.090
[93, 120] loss: 0.084
[93, 180] loss: 0.090
[93, 240] loss: 0.083
[93, 300] loss: 0.090
[93, 360] loss: 0.088
Epoch: 93 -> Loss: 0.122134879231
Epoch: 93 -> Test Accuracy: 86.46
[94, 60] loss: 0.089
[94, 120] loss: 0.086
[94, 180] loss: 0.084
[94, 240] loss: 0.090
[94, 300] loss: 0.088
[94, 360] loss: 0.086
Epoch: 94 -> Loss: 0.0983213037252
Epoch: 94 -> Test Accuracy: 86.52
[95, 60] loss: 0.083
[95, 120] loss: 0.085
[95, 180] loss: 0.084
[95, 240] loss: 0.088
[95, 300] loss: 0.089
[95, 360] loss: 0.088
Epoch: 95 -> Loss: 0.100874565542
Epoch: 95 -> Test Accuracy: 86.3
[96, 60] loss: 0.086
[96, 120] loss: 0.086
[96, 180] loss: 0.088
[96, 240] loss: 0.087
[96, 300] loss: 0.087
[96, 360] loss: 0.088
Epoch: 96 -> Loss: 0.0596638396382
Epoch: 96 -> Test Accuracy: 86.43
[97, 60] loss: 0.085
[97, 120] loss: 0.083
[97, 180] loss: 0.084
[97, 240] loss: 0.084
[97, 300] loss: 0.085
[97, 360] loss: 0.086
Epoch: 97 -> Loss: 0.104961775243
Epoch: 97 -> Test Accuracy: 86.52
[98, 60] loss: 0.083
[98, 120] loss: 0.086
[98, 180] loss: 0.088
[98, 240] loss: 0.088
[98, 300] loss: 0.086
[98, 360] loss: 0.082
Epoch: 98 -> Loss: 0.0625328570604
Epoch: 98 -> Test Accuracy: 86.51
[99, 60] loss: 0.084
[99, 120] loss: 0.085
[99, 180] loss: 0.081
[99, 240] loss: 0.085
[99, 300] loss: 0.086
[99, 360] loss: 0.092
Epoch: 99 -> Loss: 0.0963552966714
Epoch: 99 -> Test Accuracy: 86.59
[100, 60] loss: 0.084
[100, 120] loss: 0.082
[100, 180] loss: 0.082
[100, 240] loss: 0.085
[100, 300] loss: 0.087
[100, 360] loss: 0.090
Epoch: 100 -> Loss: 0.0826715677977
Epoch: 100 -> Test Accuracy: 86.58
Finished Training
[1, 60] loss: 0.933
[1, 120] loss: 0.640
[1, 180] loss: 0.577
[1, 240] loss: 0.538
[1, 300] loss: 0.545
[1, 360] loss: 0.511
Epoch: 1 -> Loss: 0.605791211128
Epoch: 1 -> Test Accuracy: 81.44
[2, 60] loss: 0.435
[2, 120] loss: 0.434
[2, 180] loss: 0.447
[2, 240] loss: 0.456
[2, 300] loss: 0.436
[2, 360] loss: 0.420
Epoch: 2 -> Loss: 0.319448173046
Epoch: 2 -> Test Accuracy: 82.72
[3, 60] loss: 0.396
[3, 120] loss: 0.394
[3, 180] loss: 0.381
[3, 240] loss: 0.391
[3, 300] loss: 0.400
[3, 360] loss: 0.395
Epoch: 3 -> Loss: 0.357403695583
Epoch: 3 -> Test Accuracy: 83.92
[4, 60] loss: 0.348
[4, 120] loss: 0.357
[4, 180] loss: 0.371
[4, 240] loss: 0.362
[4, 300] loss: 0.357
[4, 360] loss: 0.366
Epoch: 4 -> Loss: 0.428539454937
Epoch: 4 -> Test Accuracy: 84.37
[5, 60] loss: 0.339
[5, 120] loss: 0.346
[5, 180] loss: 0.353
[5, 240] loss: 0.358
[5, 300] loss: 0.353
[5, 360] loss: 0.342
Epoch: 5 -> Loss: 0.266128063202
Epoch: 5 -> Test Accuracy: 84.48
[6, 60] loss: 0.319
[6, 120] loss: 0.326
[6, 180] loss: 0.343
[6, 240] loss: 0.344
[6, 300] loss: 0.343
[6, 360] loss: 0.336
Epoch: 6 -> Loss: 0.276509791613
Epoch: 6 -> Test Accuracy: 84.24
[7, 60] loss: 0.306
[7, 120] loss: 0.323
[7, 180] loss: 0.309
[7, 240] loss: 0.315
[7, 300] loss: 0.328
[7, 360] loss: 0.330
Epoch: 7 -> Loss: 0.277797162533
Epoch: 7 -> Test Accuracy: 84.96
[8, 60] loss: 0.295
[8, 120] loss: 0.332
[8, 180] loss: 0.302
[8, 240] loss: 0.314
[8, 300] loss: 0.319
[8, 360] loss: 0.321
Epoch: 8 -> Loss: 0.501798093319
Epoch: 8 -> Test Accuracy: 84.63
[9, 60] loss: 0.285
[9, 120] loss: 0.294
[9, 180] loss: 0.319
[9, 240] loss: 0.304
[9, 300] loss: 0.313
[9, 360] loss: 0.315
Epoch: 9 -> Loss: 0.305730760098
Epoch: 9 -> Test Accuracy: 85.47
[10, 60] loss: 0.279
[10, 120] loss: 0.302
[10, 180] loss: 0.294
[10, 240] loss: 0.309
[10, 300] loss: 0.313
[10, 360] loss: 0.308
Epoch: 10 -> Loss: 0.302999407053
Epoch: 10 -> Test Accuracy: 84.77
[11, 60] loss: 0.286
[11, 120] loss: 0.296
[11, 180] loss: 0.293
[11, 240] loss: 0.299
[11, 300] loss: 0.312
[11, 360] loss: 0.303
Epoch: 11 -> Loss: 0.191775470972
Epoch: 11 -> Test Accuracy: 85.41
[12, 60] loss: 0.265
[12, 120] loss: 0.265
[12, 180] loss: 0.298
[12, 240] loss: 0.298
[12, 300] loss: 0.308
[12, 360] loss: 0.305
Epoch: 12 -> Loss: 0.256272435188
Epoch: 12 -> Test Accuracy: 85.58
[13, 60] loss: 0.266
[13, 120] loss: 0.275
[13, 180] loss: 0.281
[13, 240] loss: 0.288
[13, 300] loss: 0.295
[13, 360] loss: 0.286
Epoch: 13 -> Loss: 0.412027359009
Epoch: 13 -> Test Accuracy: 85.37
[14, 60] loss: 0.262
[14, 120] loss: 0.269
[14, 180] loss: 0.281
[14, 240] loss: 0.288
[14, 300] loss: 0.303
[14, 360] loss: 0.286
Epoch: 14 -> Loss: 0.290439993143
Epoch: 14 -> Test Accuracy: 85.46
[15, 60] loss: 0.257
[15, 120] loss: 0.277
[15, 180] loss: 0.294
[15, 240] loss: 0.300
[15, 300] loss: 0.293
[15, 360] loss: 0.290
Epoch: 15 -> Loss: 0.317489922047
Epoch: 15 -> Test Accuracy: 85.22
[16, 60] loss: 0.271
[16, 120] loss: 0.267
[16, 180] loss: 0.266
[16, 240] loss: 0.283
[16, 300] loss: 0.301
[16, 360] loss: 0.295
Epoch: 16 -> Loss: 0.270550519228
Epoch: 16 -> Test Accuracy: 85.21
[17, 60] loss: 0.258
[17, 120] loss: 0.266
[17, 180] loss: 0.262
[17, 240] loss: 0.282
[17, 300] loss: 0.305
[17, 360] loss: 0.298
Epoch: 17 -> Loss: 0.317822188139
Epoch: 17 -> Test Accuracy: 85.85
[18, 60] loss: 0.249
[18, 120] loss: 0.273
[18, 180] loss: 0.274
[18, 240] loss: 0.295
[18, 300] loss: 0.282
[18, 360] loss: 0.287
Epoch: 18 -> Loss: 0.314208477736
Epoch: 18 -> Test Accuracy: 85.97
[19, 60] loss: 0.267
[19, 120] loss: 0.250
[19, 180] loss: 0.267
[19, 240] loss: 0.269
[19, 300] loss: 0.284
[19, 360] loss: 0.281
Epoch: 19 -> Loss: 0.19237627089
Epoch: 19 -> Test Accuracy: 85.6
[20, 60] loss: 0.254
[20, 120] loss: 0.264
[20, 180] loss: 0.265
[20, 240] loss: 0.275
[20, 300] loss: 0.292
[20, 360] loss: 0.284
Epoch: 20 -> Loss: 0.385714143515
Epoch: 20 -> Test Accuracy: 85.48
[21, 60] loss: 0.261
[21, 120] loss: 0.257
[21, 180] loss: 0.244
[21, 240] loss: 0.274
[21, 300] loss: 0.298
[21, 360] loss: 0.290
Epoch: 21 -> Loss: 0.356195032597
Epoch: 21 -> Test Accuracy: 86.2
[22, 60] loss: 0.254
[22, 120] loss: 0.251
[22, 180] loss: 0.262
[22, 240] loss: 0.272
[22, 300] loss: 0.293
[22, 360] loss: 0.265
Epoch: 22 -> Loss: 0.266731321812
Epoch: 22 -> Test Accuracy: 85.57
[23, 60] loss: 0.237
[23, 120] loss: 0.254
[23, 180] loss: 0.269
[23, 240] loss: 0.293
[23, 300] loss: 0.266
[23, 360] loss: 0.291
Epoch: 23 -> Loss: 0.296865284443
Epoch: 23 -> Test Accuracy: 85.48
[24, 60] loss: 0.242
[24, 120] loss: 0.267
[24, 180] loss: 0.269
[24, 240] loss: 0.271
[24, 300] loss: 0.289
[24, 360] loss: 0.283
Epoch: 24 -> Loss: 0.424262434244
Epoch: 24 -> Test Accuracy: 86.0
[25, 60] loss: 0.256
[25, 120] loss: 0.248
[25, 180] loss: 0.249
[25, 240] loss: 0.263
[25, 300] loss: 0.274
[25, 360] loss: 0.278
Epoch: 25 -> Loss: 0.484404802322
Epoch: 25 -> Test Accuracy: 85.9
[26, 60] loss: 0.254
[26, 120] loss: 0.251
[26, 180] loss: 0.257
[26, 240] loss: 0.275
[26, 300] loss: 0.282
[26, 360] loss: 0.276
Epoch: 26 -> Loss: 0.31919452548
Epoch: 26 -> Test Accuracy: 85.45
[27, 60] loss: 0.251
[27, 120] loss: 0.263
[27, 180] loss: 0.255
[27, 240] loss: 0.269
[27, 300] loss: 0.265
[27, 360] loss: 0.280
Epoch: 27 -> Loss: 0.288360774517
Epoch: 27 -> Test Accuracy: 85.97
[28, 60] loss: 0.228
[28, 120] loss: 0.259
[28, 180] loss: 0.267
[28, 240] loss: 0.271
[28, 300] loss: 0.283
[28, 360] loss: 0.278
Epoch: 28 -> Loss: 0.303036868572
Epoch: 28 -> Test Accuracy: 85.12
[29, 60] loss: 0.248
[29, 120] loss: 0.253
[29, 180] loss: 0.269
[29, 240] loss: 0.263
[29, 300] loss: 0.277
[29, 360] loss: 0.267
Epoch: 29 -> Loss: 0.328431010246
Epoch: 29 -> Test Accuracy: 85.1
[30, 60] loss: 0.250
[30, 120] loss: 0.230
[30, 180] loss: 0.274
[30, 240] loss: 0.278
[30, 300] loss: 0.272
[30, 360] loss: 0.285
Epoch: 30 -> Loss: 0.297442853451
Epoch: 30 -> Test Accuracy: 85.47
[31, 60] loss: 0.241
[31, 120] loss: 0.241
[31, 180] loss: 0.254
[31, 240] loss: 0.266
[31, 300] loss: 0.284
[31, 360] loss: 0.291
Epoch: 31 -> Loss: 0.275793671608
Epoch: 31 -> Test Accuracy: 85.57
[32, 60] loss: 0.234
[32, 120] loss: 0.257
[32, 180] loss: 0.252
[32, 240] loss: 0.264
[32, 300] loss: 0.278
[32, 360] loss: 0.286
Epoch: 32 -> Loss: 0.310254454613
Epoch: 32 -> Test Accuracy: 85.51
[33, 60] loss: 0.234
[33, 120] loss: 0.250
[33, 180] loss: 0.267
[33, 240] loss: 0.263
[33, 300] loss: 0.269
[33, 360] loss: 0.278
Epoch: 33 -> Loss: 0.424141407013
Epoch: 33 -> Test Accuracy: 85.2
[34, 60] loss: 0.228
[34, 120] loss: 0.240
[34, 180] loss: 0.257
[34, 240] loss: 0.272
[34, 300] loss: 0.271
[34, 360] loss: 0.275
Epoch: 34 -> Loss: 0.342402011156
Epoch: 34 -> Test Accuracy: 85.96
[35, 60] loss: 0.233
[35, 120] loss: 0.242
[35, 180] loss: 0.274
[35, 240] loss: 0.273
[35, 300] loss: 0.263
[35, 360] loss: 0.260
Epoch: 35 -> Loss: 0.506009280682
Epoch: 35 -> Test Accuracy: 85.96
[36, 60] loss: 0.202
[36, 120] loss: 0.186
[36, 180] loss: 0.174
[36, 240] loss: 0.174
[36, 300] loss: 0.154
[36, 360] loss: 0.170
Epoch: 36 -> Loss: 0.179405838251
Epoch: 36 -> Test Accuracy: 88.45
[37, 60] loss: 0.150
[37, 120] loss: 0.144
[37, 180] loss: 0.151
[37, 240] loss: 0.136
[37, 300] loss: 0.142
[37, 360] loss: 0.138
Epoch: 37 -> Loss: 0.0837014690042
Epoch: 37 -> Test Accuracy: 88.58
[38, 60] loss: 0.126
[38, 120] loss: 0.134
[38, 180] loss: 0.128
[38, 240] loss: 0.126
[38, 300] loss: 0.140
[38, 360] loss: 0.131
Epoch: 38 -> Loss: 0.134751647711
Epoch: 38 -> Test Accuracy: 88.53
[39, 60] loss: 0.123
[39, 120] loss: 0.124
[39, 180] loss: 0.112
[39, 240] loss: 0.126
[39, 300] loss: 0.123
[39, 360] loss: 0.125
Epoch: 39 -> Loss: 0.0824265927076
Epoch: 39 -> Test Accuracy: 88.36
[40, 60] loss: 0.114
[40, 120] loss: 0.109
[40, 180] loss: 0.116
[40, 240] loss: 0.115
[40, 300] loss: 0.122
[40, 360] loss: 0.128
Epoch: 40 -> Loss: 0.21214401722
Epoch: 40 -> Test Accuracy: 88.31
[41, 60] loss: 0.104
[41, 120] loss: 0.110
[41, 180] loss: 0.110
[41, 240] loss: 0.119
[41, 300] loss: 0.119
[41, 360] loss: 0.114
Epoch: 41 -> Loss: 0.263169586658
Epoch: 41 -> Test Accuracy: 88.04
[42, 60] loss: 0.104
[42, 120] loss: 0.105
[42, 180] loss: 0.111
[42, 240] loss: 0.117
[42, 300] loss: 0.106
[42, 360] loss: 0.111
Epoch: 42 -> Loss: 0.153781086206
Epoch: 42 -> Test Accuracy: 88.05
[43, 60] loss: 0.097
[43, 120] loss: 0.108
[43, 180] loss: 0.108
[43, 240] loss: 0.107
[43, 300] loss: 0.101
[43, 360] loss: 0.112
Epoch: 43 -> Loss: 0.156316131353
Epoch: 43 -> Test Accuracy: 87.71
[44, 60] loss: 0.097
[44, 120] loss: 0.106
[44, 180] loss: 0.107
[44, 240] loss: 0.108
[44, 300] loss: 0.111
[44, 360] loss: 0.106
Epoch: 44 -> Loss: 0.15221658349
Epoch: 44 -> Test Accuracy: 87.62
[45, 60] loss: 0.098
[45, 120] loss: 0.099
[45, 180] loss: 0.103
[45, 240] loss: 0.112
[45, 300] loss: 0.115
[45, 360] loss: 0.122
Epoch: 45 -> Loss: 0.0631106942892
Epoch: 45 -> Test Accuracy: 87.64
[46, 60] loss: 0.099
[46, 120] loss: 0.101
[46, 180] loss: 0.105
[46, 240] loss: 0.101
[46, 300] loss: 0.119
[46, 360] loss: 0.118
Epoch: 46 -> Loss: 0.109301820397
Epoch: 46 -> Test Accuracy: 87.78
[47, 60] loss: 0.098
[47, 120] loss: 0.096
[47, 180] loss: 0.111
[47, 240] loss: 0.109
[47, 300] loss: 0.112
[47, 360] loss: 0.118
Epoch: 47 -> Loss: 0.116154432297
Epoch: 47 -> Test Accuracy: 88.05
[48, 60] loss: 0.096
[48, 120] loss: 0.097
[48, 180] loss: 0.112
[48, 240] loss: 0.108
[48, 300] loss: 0.105
[48, 360] loss: 0.114
Epoch: 48 -> Loss: 0.0701258927584
Epoch: 48 -> Test Accuracy: 87.52
[49, 60] loss: 0.097
[49, 120] loss: 0.108
[49, 180] loss: 0.105
[49, 240] loss: 0.101
[49, 300] loss: 0.113
[49, 360] loss: 0.118
Epoch: 49 -> Loss: 0.0811708495021
Epoch: 49 -> Test Accuracy: 87.66
[50, 60] loss: 0.098
[50, 120] loss: 0.098
[50, 180] loss: 0.106
[50, 240] loss: 0.112
[50, 300] loss: 0.113
[50, 360] loss: 0.119
Epoch: 50 -> Loss: 0.165228754282
Epoch: 50 -> Test Accuracy: 87.59
[51, 60] loss: 0.097
[51, 120] loss: 0.096
[51, 180] loss: 0.105
[51, 240] loss: 0.111
[51, 300] loss: 0.117
[51, 360] loss: 0.106
Epoch: 51 -> Loss: 0.132050231099
Epoch: 51 -> Test Accuracy: 87.75
[52, 60] loss: 0.096
[52, 120] loss: 0.094
[52, 180] loss: 0.109
[52, 240] loss: 0.116
[52, 300] loss: 0.121
[52, 360] loss: 0.107
Epoch: 52 -> Loss: 0.0968780368567
Epoch: 52 -> Test Accuracy: 87.16
[53, 60] loss: 0.104
[53, 120] loss: 0.094
[53, 180] loss: 0.112
[53, 240] loss: 0.112
[53, 300] loss: 0.107
[53, 360] loss: 0.114
Epoch: 53 -> Loss: 0.0859619155526
Epoch: 53 -> Test Accuracy: 86.93
[54, 60] loss: 0.096
[54, 120] loss: 0.101
[54, 180] loss: 0.106
[54, 240] loss: 0.110
[54, 300] loss: 0.119
[54, 360] loss: 0.127
Epoch: 54 -> Loss: 0.139895915985
Epoch: 54 -> Test Accuracy: 87.57
[55, 60] loss: 0.099
[55, 120] loss: 0.096
[55, 180] loss: 0.111
[55, 240] loss: 0.113
[55, 300] loss: 0.106
[55, 360] loss: 0.111
Epoch: 55 -> Loss: 0.0857071876526
Epoch: 55 -> Test Accuracy: 87.28
[56, 60] loss: 0.105
[56, 120] loss: 0.114
[56, 180] loss: 0.110
[56, 240] loss: 0.109
[56, 300] loss: 0.116
[56, 360] loss: 0.113
Epoch: 56 -> Loss: 0.0610634274781
Epoch: 56 -> Test Accuracy: 87.17
[57, 60] loss: 0.100
[57, 120] loss: 0.105
[57, 180] loss: 0.105
[57, 240] loss: 0.108
[57, 300] loss: 0.116
[57, 360] loss: 0.111
Epoch: 57 -> Loss: 0.102903082967
Epoch: 57 -> Test Accuracy: 87.25
[58, 60] loss: 0.099
[58, 120] loss: 0.105
[58, 180] loss: 0.107
[58, 240] loss: 0.109
[58, 300] loss: 0.108
[58, 360] loss: 0.107
Epoch: 58 -> Loss: 0.158154025674
Epoch: 58 -> Test Accuracy: 86.9
[59, 60] loss: 0.094
[59, 120] loss: 0.106
[59, 180] loss: 0.102
[59, 240] loss: 0.105
[59, 300] loss: 0.125
[59, 360] loss: 0.110
Epoch: 59 -> Loss: 0.224472165108
Epoch: 59 -> Test Accuracy: 87.05
[60, 60] loss: 0.097
[60, 120] loss: 0.098
[60, 180] loss: 0.103
[60, 240] loss: 0.102
[60, 300] loss: 0.110
[60, 360] loss: 0.113
Epoch: 60 -> Loss: 0.148394331336
Epoch: 60 -> Test Accuracy: 87.09
[61, 60] loss: 0.106
[61, 120] loss: 0.103
[61, 180] loss: 0.098
[61, 240] loss: 0.116
[61, 300] loss: 0.118
[61, 360] loss: 0.111
Epoch: 61 -> Loss: 0.0785834789276
Epoch: 61 -> Test Accuracy: 86.8
[62, 60] loss: 0.110
[62, 120] loss: 0.095
[62, 180] loss: 0.100
[62, 240] loss: 0.099
[62, 300] loss: 0.107
[62, 360] loss: 0.116
Epoch: 62 -> Loss: 0.135267615318
Epoch: 62 -> Test Accuracy: 87.04
[63, 60] loss: 0.094
[63, 120] loss: 0.097
[63, 180] loss: 0.101
[63, 240] loss: 0.106
[63, 300] loss: 0.103
[63, 360] loss: 0.108
Epoch: 63 -> Loss: 0.0476409755647
Epoch: 63 -> Test Accuracy: 87.28
[64, 60] loss: 0.107
[64, 120] loss: 0.104
[64, 180] loss: 0.103
[64, 240] loss: 0.112
[64, 300] loss: 0.113
[64, 360] loss: 0.111
Epoch: 64 -> Loss: 0.140357613564
Epoch: 64 -> Test Accuracy: 87.35
[65, 60] loss: 0.090
[65, 120] loss: 0.092
[65, 180] loss: 0.119
[65, 240] loss: 0.109
[65, 300] loss: 0.115
[65, 360] loss: 0.110
Epoch: 65 -> Loss: 0.0916037410498
Epoch: 65 -> Test Accuracy: 87.69
[66, 60] loss: 0.094
[66, 120] loss: 0.096
[66, 180] loss: 0.102
[66, 240] loss: 0.102
[66, 300] loss: 0.105
[66, 360] loss: 0.114
Epoch: 66 -> Loss: 0.192954391241
Epoch: 66 -> Test Accuracy: 86.62
[67, 60] loss: 0.095
[67, 120] loss: 0.099
[67, 180] loss: 0.096
[67, 240] loss: 0.108
[67, 300] loss: 0.108
[67, 360] loss: 0.106
Epoch: 67 -> Loss: 0.109978698194
Epoch: 67 -> Test Accuracy: 87.2
[68, 60] loss: 0.094
[68, 120] loss: 0.094
[68, 180] loss: 0.097
[68, 240] loss: 0.105
[68, 300] loss: 0.109
[68, 360] loss: 0.108
Epoch: 68 -> Loss: 0.115408338606
Epoch: 68 -> Test Accuracy: 87.24
[69, 60] loss: 0.095
[69, 120] loss: 0.105
[69, 180] loss: 0.103
[69, 240] loss: 0.103
[69, 300] loss: 0.098
[69, 360] loss: 0.118
Epoch: 69 -> Loss: 0.173121526837
Epoch: 69 -> Test Accuracy: 87.27
[70, 60] loss: 0.090
[70, 120] loss: 0.090
[70, 180] loss: 0.103
[70, 240] loss: 0.105
[70, 300] loss: 0.103
[70, 360] loss: 0.114
Epoch: 70 -> Loss: 0.13461714983
Epoch: 70 -> Test Accuracy: 87.43
[71, 60] loss: 0.078
[71, 120] loss: 0.070
[71, 180] loss: 0.066
[71, 240] loss: 0.064
[71, 300] loss: 0.061
[71, 360] loss: 0.057
Epoch: 71 -> Loss: 0.0930706188083
Epoch: 71 -> Test Accuracy: 88.63
[72, 60] loss: 0.048
[72, 120] loss: 0.052
[72, 180] loss: 0.053
[72, 240] loss: 0.052
[72, 300] loss: 0.054
[72, 360] loss: 0.057
Epoch: 72 -> Loss: 0.0489098206162
Epoch: 72 -> Test Accuracy: 88.85
[73, 60] loss: 0.047
[73, 120] loss: 0.046
[73, 180] loss: 0.047
[73, 240] loss: 0.047
[73, 300] loss: 0.054
[73, 360] loss: 0.050
Epoch: 73 -> Loss: 0.0769120901823
Epoch: 73 -> Test Accuracy: 88.64
[74, 60] loss: 0.047
[74, 120] loss: 0.050
[74, 180] loss: 0.045
[74, 240] loss: 0.047
[74, 300] loss: 0.041
[74, 360] loss: 0.041
Epoch: 74 -> Loss: 0.069330945611
Epoch: 74 -> Test Accuracy: 88.6
[75, 60] loss: 0.042
[75, 120] loss: 0.040
[75, 180] loss: 0.045
[75, 240] loss: 0.046
[75, 300] loss: 0.042
[75, 360] loss: 0.042
Epoch: 75 -> Loss: 0.0492066368461
Epoch: 75 -> Test Accuracy: 89.07
[76, 60] loss: 0.037
[76, 120] loss: 0.037
[76, 180] loss: 0.046
[76, 240] loss: 0.042
[76, 300] loss: 0.038
[76, 360] loss: 0.038
Epoch: 76 -> Loss: 0.0620439946651
Epoch: 76 -> Test Accuracy: 88.78
[77, 60] loss: 0.037
[77, 120] loss: 0.038
[77, 180] loss: 0.038
[77, 240] loss: 0.040
[77, 300] loss: 0.041
[77, 360] loss: 0.040
Epoch: 77 -> Loss: 0.0635176822543
Epoch: 77 -> Test Accuracy: 88.66
[78, 60] loss: 0.037
[78, 120] loss: 0.035
[78, 180] loss: 0.035
[78, 240] loss: 0.036
[78, 300] loss: 0.039
[78, 360] loss: 0.038
Epoch: 78 -> Loss: 0.0321986787021
Epoch: 78 -> Test Accuracy: 88.69
[79, 60] loss: 0.037
[79, 120] loss: 0.037
[79, 180] loss: 0.039
[79, 240] loss: 0.038
[79, 300] loss: 0.036
[79, 360] loss: 0.036
Epoch: 79 -> Loss: 0.0234310366213
Epoch: 79 -> Test Accuracy: 88.74
[80, 60] loss: 0.035
[80, 120] loss: 0.034
[80, 180] loss: 0.033
[80, 240] loss: 0.034
[80, 300] loss: 0.036
[80, 360] loss: 0.036
Epoch: 80 -> Loss: 0.0715058892965
Epoch: 80 -> Test Accuracy: 88.65
[81, 60] loss: 0.032
[81, 120] loss: 0.034
[81, 180] loss: 0.034
[81, 240] loss: 0.036
[81, 300] loss: 0.037
[81, 360] loss: 0.037
Epoch: 81 -> Loss: 0.0276502557099
Epoch: 81 -> Test Accuracy: 88.6
[82, 60] loss: 0.032
[82, 120] loss: 0.031
[82, 180] loss: 0.035
[82, 240] loss: 0.033
[82, 300] loss: 0.031
[82, 360] loss: 0.037
Epoch: 82 -> Loss: 0.0351563431323
Epoch: 82 -> Test Accuracy: 88.44
[83, 60] loss: 0.031
[83, 120] loss: 0.032
[83, 180] loss: 0.033
[83, 240] loss: 0.033
[83, 300] loss: 0.036
[83, 360] loss: 0.036
Epoch: 83 -> Loss: 0.0192641858011
Epoch: 83 -> Test Accuracy: 88.77
[84, 60] loss: 0.030
[84, 120] loss: 0.032
[84, 180] loss: 0.032
[84, 240] loss: 0.033
[84, 300] loss: 0.033
[84, 360] loss: 0.032
Epoch: 84 -> Loss: 0.0821533873677
Epoch: 84 -> Test Accuracy: 88.63
[85, 60] loss: 0.032
[85, 120] loss: 0.032
[85, 180] loss: 0.031
[85, 240] loss: 0.029
[85, 300] loss: 0.034
[85, 360] loss: 0.032
Epoch: 85 -> Loss: 0.0377970822155
Epoch: 85 -> Test Accuracy: 88.53
[86, 60] loss: 0.029
[86, 120] loss: 0.033
[86, 180] loss: 0.028
[86, 240] loss: 0.026
[86, 300] loss: 0.030
[86, 360] loss: 0.026
Epoch: 86 -> Loss: 0.021526414901
Epoch: 86 -> Test Accuracy: 88.69
[87, 60] loss: 0.025
[87, 120] loss: 0.028
[87, 180] loss: 0.025
[87, 240] loss: 0.027
[87, 300] loss: 0.028
[87, 360] loss: 0.028
Epoch: 87 -> Loss: 0.014181887731
Epoch: 87 -> Test Accuracy: 88.59
[88, 60] loss: 0.027
[88, 120] loss: 0.026
[88, 180] loss: 0.028
[88, 240] loss: 0.028
[88, 300] loss: 0.029
[88, 360] loss: 0.029
Epoch: 88 -> Loss: 0.0407947227359
Epoch: 88 -> Test Accuracy: 88.67
[89, 60] loss: 0.025
[89, 120] loss: 0.026
[89, 180] loss: 0.028
[89, 240] loss: 0.027
[89, 300] loss: 0.026
[89, 360] loss: 0.028
Epoch: 89 -> Loss: 0.0337344445288
Epoch: 89 -> Test Accuracy: 88.64
[90, 60] loss: 0.026
[90, 120] loss: 0.029
[90, 180] loss: 0.025
[90, 240] loss: 0.027
[90, 300] loss: 0.026
[90, 360] loss: 0.025
Epoch: 90 -> Loss: 0.0341849885881
Epoch: 90 -> Test Accuracy: 88.64
[91, 60] loss: 0.030
[91, 120] loss: 0.027
[91, 180] loss: 0.030
[91, 240] loss: 0.027
[91, 300] loss: 0.027
[91, 360] loss: 0.027
Epoch: 91 -> Loss: 0.0218796730042
Epoch: 91 -> Test Accuracy: 88.8
[92, 60] loss: 0.028
[92, 120] loss: 0.024
[92, 180] loss: 0.028
[92, 240] loss: 0.025
[92, 300] loss: 0.030
[92, 360] loss: 0.028
Epoch: 92 -> Loss: 0.0230861660093
Epoch: 92 -> Test Accuracy: 88.66
[93, 60] loss: 0.028
[93, 120] loss: 0.028
[93, 180] loss: 0.024
[93, 240] loss: 0.025
[93, 300] loss: 0.028
[93, 360] loss: 0.026
Epoch: 93 -> Loss: 0.0308908764273
Epoch: 93 -> Test Accuracy: 88.7
[94, 60] loss: 0.025
[94, 120] loss: 0.026
[94, 180] loss: 0.025
[94, 240] loss: 0.024
[94, 300] loss: 0.026
[94, 360] loss: 0.027
Epoch: 94 -> Loss: 0.0687903538346
Epoch: 94 -> Test Accuracy: 88.73
[95, 60] loss: 0.027
[95, 120] loss: 0.027
[95, 180] loss: 0.025
[95, 240] loss: 0.026
[95, 300] loss: 0.028
[95, 360] loss: 0.026
Epoch: 95 -> Loss: 0.0634961277246
Epoch: 95 -> Test Accuracy: 88.84
[96, 60] loss: 0.026
[96, 120] loss: 0.026
[96, 180] loss: 0.028
[96, 240] loss: 0.026
[96, 300] loss: 0.025
[96, 360] loss: 0.027
Epoch: 96 -> Loss: 0.087438300252
Epoch: 96 -> Test Accuracy: 88.66
[97, 60] loss: 0.026
[97, 120] loss: 0.028
[97, 180] loss: 0.025
[97, 240] loss: 0.025
[97, 300] loss: 0.025
[97, 360] loss: 0.026
Epoch: 97 -> Loss: 0.0222023427486
Epoch: 97 -> Test Accuracy: 88.68
[98, 60] loss: 0.025
[98, 120] loss: 0.025
[98, 180] loss: 0.025
[98, 240] loss: 0.029
[98, 300] loss: 0.027
[98, 360] loss: 0.027
Epoch: 98 -> Loss: 0.0354787521064
Epoch: 98 -> Test Accuracy: 88.61
[99, 60] loss: 0.026
[99, 120] loss: 0.025
[99, 180] loss: 0.023
[99, 240] loss: 0.025
[99, 300] loss: 0.024
[99, 360] loss: 0.028
Epoch: 99 -> Loss: 0.0437642261386
Epoch: 99 -> Test Accuracy: 88.76
[100, 60] loss: 0.025
[100, 120] loss: 0.025
[100, 180] loss: 0.025
[100, 240] loss: 0.025
[100, 300] loss: 0.026
[100, 360] loss: 0.023
Epoch: 100 -> Loss: 0.0281120426953
Epoch: 100 -> Test Accuracy: 88.83
Finished Training
[1, 60] loss: 0.904
[1, 120] loss: 0.697
[1, 180] loss: 0.629
[1, 240] loss: 0.602
[1, 300] loss: 0.577
[1, 360] loss: 0.555
Epoch: 1 -> Loss: 0.638784229755
Epoch: 1 -> Test Accuracy: 77.46
[2, 60] loss: 0.518
[2, 120] loss: 0.521
[2, 180] loss: 0.529
[2, 240] loss: 0.522
[2, 300] loss: 0.513
[2, 360] loss: 0.522
Epoch: 2 -> Loss: 0.496109187603
Epoch: 2 -> Test Accuracy: 79.71
[3, 60] loss: 0.500
[3, 120] loss: 0.474
[3, 180] loss: 0.494
[3, 240] loss: 0.487
[3, 300] loss: 0.472
[3, 360] loss: 0.486
Epoch: 3 -> Loss: 0.407837331295
Epoch: 3 -> Test Accuracy: 79.51
[4, 60] loss: 0.453
[4, 120] loss: 0.473
[4, 180] loss: 0.449
[4, 240] loss: 0.465
[4, 300] loss: 0.471
[4, 360] loss: 0.471
Epoch: 4 -> Loss: 0.487863689661
Epoch: 4 -> Test Accuracy: 79.92
[5, 60] loss: 0.451
[5, 120] loss: 0.446
[5, 180] loss: 0.450
[5, 240] loss: 0.444
[5, 300] loss: 0.453
[5, 360] loss: 0.476
Epoch: 5 -> Loss: 0.385122567415
Epoch: 5 -> Test Accuracy: 80.83
[6, 60] loss: 0.430
[6, 120] loss: 0.432
[6, 180] loss: 0.461
[6, 240] loss: 0.438
[6, 300] loss: 0.448
[6, 360] loss: 0.455
Epoch: 6 -> Loss: 0.413381874561
Epoch: 6 -> Test Accuracy: 80.09
[7, 60] loss: 0.431
[7, 120] loss: 0.432
[7, 180] loss: 0.424
[7, 240] loss: 0.440
[7, 300] loss: 0.436
[7, 360] loss: 0.432
Epoch: 7 -> Loss: 0.489655166864
Epoch: 7 -> Test Accuracy: 80.93
[8, 60] loss: 0.421
[8, 120] loss: 0.420
[8, 180] loss: 0.408
[8, 240] loss: 0.439
[8, 300] loss: 0.446
[8, 360] loss: 0.442
Epoch: 8 -> Loss: 0.447660923004
Epoch: 8 -> Test Accuracy: 80.85
[9, 60] loss: 0.398
[9, 120] loss: 0.424
[9, 180] loss: 0.427
[9, 240] loss: 0.434
[9, 300] loss: 0.431
[9, 360] loss: 0.423
Epoch: 9 -> Loss: 0.618254363537
Epoch: 9 -> Test Accuracy: 80.68
[10, 60] loss: 0.419
[10, 120] loss: 0.414
[10, 180] loss: 0.413
[10, 240] loss: 0.408
[10, 300] loss: 0.422
[10, 360] loss: 0.424
Epoch: 10 -> Loss: 0.370585739613
Epoch: 10 -> Test Accuracy: 81.33
[11, 60] loss: 0.386
[11, 120] loss: 0.400
[11, 180] loss: 0.411
[11, 240] loss: 0.417
[11, 300] loss: 0.438
[11, 360] loss: 0.415
Epoch: 11 -> Loss: 0.421869128942
Epoch: 11 -> Test Accuracy: 80.73
[12, 60] loss: 0.393
[12, 120] loss: 0.393
[12, 180] loss: 0.405
[12, 240] loss: 0.434
[12, 300] loss: 0.418
[12, 360] loss: 0.412
Epoch: 12 -> Loss: 0.379435688257
Epoch: 12 -> Test Accuracy: 81.68
[13, 60] loss: 0.401
[13, 120] loss: 0.414
[13, 180] loss: 0.410
[13, 240] loss: 0.432
[13, 300] loss: 0.393
[13, 360] loss: 0.417
Epoch: 13 -> Loss: 0.507173001766
Epoch: 13 -> Test Accuracy: 80.76
[14, 60] loss: 0.401
[14, 120] loss: 0.390
[14, 180] loss: 0.406
[14, 240] loss: 0.404
[14, 300] loss: 0.403
[14, 360] loss: 0.412
Epoch: 14 -> Loss: 0.511099457741
Epoch: 14 -> Test Accuracy: 81.39
[15, 60] loss: 0.389
[15, 120] loss: 0.389
[15, 180] loss: 0.405
[15, 240] loss: 0.407
[15, 300] loss: 0.405
[15, 360] loss: 0.423
Epoch: 15 -> Loss: 0.389810353518
Epoch: 15 -> Test Accuracy: 81.74
[16, 60] loss: 0.362
[16, 120] loss: 0.416
[16, 180] loss: 0.393
[16, 240] loss: 0.407
[16, 300] loss: 0.401
[16, 360] loss: 0.405
Epoch: 16 -> Loss: 0.300886750221
Epoch: 16 -> Test Accuracy: 80.07
[17, 60] loss: 0.393
[17, 120] loss: 0.392
[17, 180] loss: 0.407
[17, 240] loss: 0.403
[17, 300] loss: 0.393
[17, 360] loss: 0.387
Epoch: 17 -> Loss: 0.41320976615
Epoch: 17 -> Test Accuracy: 81.36
[18, 60] loss: 0.379
[18, 120] loss: 0.399
[18, 180] loss: 0.410
[18, 240] loss: 0.397
[18, 300] loss: 0.398
[18, 360] loss: 0.398
Epoch: 18 -> Loss: 0.485069662333
Epoch: 18 -> Test Accuracy: 81.31
[19, 60] loss: 0.368
[19, 120] loss: 0.391
[19, 180] loss: 0.408
[19, 240] loss: 0.403
[19, 300] loss: 0.414
[19, 360] loss: 0.390
Epoch: 19 -> Loss: 0.283340185881
Epoch: 19 -> Test Accuracy: 80.93
[20, 60] loss: 0.372
[20, 120] loss: 0.394
[20, 180] loss: 0.390
[20, 240] loss: 0.387
[20, 300] loss: 0.398
[20, 360] loss: 0.410
Epoch: 20 -> Loss: 0.516863584518
Epoch: 20 -> Test Accuracy: 80.23
[21, 60] loss: 0.383
[21, 120] loss: 0.379
[21, 180] loss: 0.394
[21, 240] loss: 0.390
[21, 300] loss: 0.397
[21, 360] loss: 0.417
Epoch: 21 -> Loss: 0.2647113204
Epoch: 21 -> Test Accuracy: 81.77
[22, 60] loss: 0.384
[22, 120] loss: 0.395
[22, 180] loss: 0.396
[22, 240] loss: 0.407
[22, 300] loss: 0.391
[22, 360] loss: 0.370
Epoch: 22 -> Loss: 0.390468150377
Epoch: 22 -> Test Accuracy: 82.19
[23, 60] loss: 0.377
[23, 120] loss: 0.375
[23, 180] loss: 0.375
[23, 240] loss: 0.404
[23, 300] loss: 0.406
[23, 360] loss: 0.397
Epoch: 23 -> Loss: 0.513204038143
Epoch: 23 -> Test Accuracy: 80.95
[24, 60] loss: 0.369
[24, 120] loss: 0.394
[24, 180] loss: 0.393
[24, 240] loss: 0.400
[24, 300] loss: 0.386
[24, 360] loss: 0.408
Epoch: 24 -> Loss: 0.329729020596
Epoch: 24 -> Test Accuracy: 81.18
[25, 60] loss: 0.374
[25, 120] loss: 0.379
[25, 180] loss: 0.365
[25, 240] loss: 0.403
[25, 300] loss: 0.391
[25, 360] loss: 0.404
Epoch: 25 -> Loss: 0.560485541821
Epoch: 25 -> Test Accuracy: 81.52
[26, 60] loss: 0.372
[26, 120] loss: 0.399
[26, 180] loss: 0.384
[26, 240] loss: 0.377
[26, 300] loss: 0.383
[26, 360] loss: 0.392
Epoch: 26 -> Loss: 0.372234463692
Epoch: 26 -> Test Accuracy: 81.32
[27, 60] loss: 0.389
[27, 120] loss: 0.379
[27, 180] loss: 0.387
[27, 240] loss: 0.393
[27, 300] loss: 0.379
[27, 360] loss: 0.391
Epoch: 27 -> Loss: 0.481274425983
Epoch: 27 -> Test Accuracy: 81.36
[28, 60] loss: 0.381
[28, 120] loss: 0.361
[28, 180] loss: 0.394
[28, 240] loss: 0.401
[28, 300] loss: 0.394
[28, 360] loss: 0.388
Epoch: 28 -> Loss: 0.351651012897
Epoch: 28 -> Test Accuracy: 81.88
[29, 60] loss: 0.371
[29, 120] loss: 0.373
[29, 180] loss: 0.375
[29, 240] loss: 0.375
[29, 300] loss: 0.417
[29, 360] loss: 0.399
Epoch: 29 -> Loss: 0.550917267799
Epoch: 29 -> Test Accuracy: 81.18
[30, 60] loss: 0.356
[30, 120] loss: 0.377
[30, 180] loss: 0.388
[30, 240] loss: 0.384
[30, 300] loss: 0.416
[30, 360] loss: 0.398
Epoch: 30 -> Loss: 0.294458210468
Epoch: 30 -> Test Accuracy: 81.2
[31, 60] loss: 0.373
[31, 120] loss: 0.386
[31, 180] loss: 0.360
[31, 240] loss: 0.410
[31, 300] loss: 0.390
[31, 360] loss: 0.393
Epoch: 31 -> Loss: 0.384102076292
Epoch: 31 -> Test Accuracy: 81.13
[32, 60] loss: 0.376
[32, 120] loss: 0.369
[32, 180] loss: 0.389
[32, 240] loss: 0.399
[32, 300] loss: 0.379
[32, 360] loss: 0.399
Epoch: 32 -> Loss: 0.405167967081
Epoch: 32 -> Test Accuracy: 81.24
[33, 60] loss: 0.361
[33, 120] loss: 0.394
[33, 180] loss: 0.382
[33, 240] loss: 0.378
[33, 300] loss: 0.400
[33, 360] loss: 0.391
Epoch: 33 -> Loss: 0.305773496628
Epoch: 33 -> Test Accuracy: 81.49
[34, 60] loss: 0.354
[34, 120] loss: 0.383
[34, 180] loss: 0.378
[34, 240] loss: 0.388
[34, 300] loss: 0.400
[34, 360] loss: 0.405
Epoch: 34 -> Loss: 0.448006093502
Epoch: 34 -> Test Accuracy: 81.39
[35, 60] loss: 0.352
[35, 120] loss: 0.389
[35, 180] loss: 0.385
[35, 240] loss: 0.379
[35, 300] loss: 0.375
[35, 360] loss: 0.398
Epoch: 35 -> Loss: 0.533167719841
Epoch: 35 -> Test Accuracy: 81.25
[36, 60] loss: 0.333
[36, 120] loss: 0.300
[36, 180] loss: 0.312
[36, 240] loss: 0.284
[36, 300] loss: 0.293
[36, 360] loss: 0.284
Epoch: 36 -> Loss: 0.323698431253
Epoch: 36 -> Test Accuracy: 84.19
[37, 60] loss: 0.279
[37, 120] loss: 0.283
[37, 180] loss: 0.273
[37, 240] loss: 0.278
[37, 300] loss: 0.286
[37, 360] loss: 0.276
Epoch: 37 -> Loss: 0.271359354258
Epoch: 37 -> Test Accuracy: 84.06
[38, 60] loss: 0.259
[38, 120] loss: 0.271
[38, 180] loss: 0.272
[38, 240] loss: 0.264
[38, 300] loss: 0.270
[38, 360] loss: 0.271
Epoch: 38 -> Loss: 0.403861284256
Epoch: 38 -> Test Accuracy: 83.78
[39, 60] loss: 0.247
[39, 120] loss: 0.258
[39, 180] loss: 0.234
[39, 240] loss: 0.253
[39, 300] loss: 0.261
[39, 360] loss: 0.257
Epoch: 39 -> Loss: 0.294805347919
Epoch: 39 -> Test Accuracy: 83.54
[40, 60] loss: 0.244
[40, 120] loss: 0.254
[40, 180] loss: 0.255
[40, 240] loss: 0.252
[40, 300] loss: 0.253
[40, 360] loss: 0.269
Epoch: 40 -> Loss: 0.23317758739
Epoch: 40 -> Test Accuracy: 83.88
[41, 60] loss: 0.249
[41, 120] loss: 0.245
[41, 180] loss: 0.246
[41, 240] loss: 0.248
[41, 300] loss: 0.256
[41, 360] loss: 0.269
Epoch: 41 -> Loss: 0.188509583473
Epoch: 41 -> Test Accuracy: 84.17
[42, 60] loss: 0.247
[42, 120] loss: 0.253
[42, 180] loss: 0.248
[42, 240] loss: 0.245
[42, 300] loss: 0.253
[42, 360] loss: 0.256
Epoch: 42 -> Loss: 0.288965761662
Epoch: 42 -> Test Accuracy: 83.76
[43, 60] loss: 0.233
[43, 120] loss: 0.248
[43, 180] loss: 0.244
[43, 240] loss: 0.262
[43, 300] loss: 0.244
[43, 360] loss: 0.258
Epoch: 43 -> Loss: 0.280441105366
Epoch: 43 -> Test Accuracy: 83.36
[44, 60] loss: 0.236
[44, 120] loss: 0.248
[44, 180] loss: 0.248
[44, 240] loss: 0.249
[44, 300] loss: 0.254
[44, 360] loss: 0.239
Epoch: 44 -> Loss: 0.329467117786
Epoch: 44 -> Test Accuracy: 83.52
[45, 60] loss: 0.239
[45, 120] loss: 0.226
[45, 180] loss: 0.243
[45, 240] loss: 0.236
[45, 300] loss: 0.264
[45, 360] loss: 0.260
Epoch: 45 -> Loss: 0.202561542392
Epoch: 45 -> Test Accuracy: 83.42
[46, 60] loss: 0.235
[46, 120] loss: 0.233
[46, 180] loss: 0.244
[46, 240] loss: 0.243
[46, 300] loss: 0.254
[46, 360] loss: 0.254
Epoch: 46 -> Loss: 0.427877992392
Epoch: 46 -> Test Accuracy: 83.4
[47, 60] loss: 0.229
[47, 120] loss: 0.246
[47, 180] loss: 0.236
[47, 240] loss: 0.231
[47, 300] loss: 0.252
[47, 360] loss: 0.263
Epoch: 47 -> Loss: 0.337016016245
Epoch: 47 -> Test Accuracy: 83.13
[48, 60] loss: 0.228
[48, 120] loss: 0.223
[48, 180] loss: 0.239
[48, 240] loss: 0.262
[48, 300] loss: 0.242
[48, 360] loss: 0.252
Epoch: 48 -> Loss: 0.292108207941
Epoch: 48 -> Test Accuracy: 82.98
[49, 60] loss: 0.232
[49, 120] loss: 0.235
[49, 180] loss: 0.244
[49, 240] loss: 0.255
[49, 300] loss: 0.236
[49, 360] loss: 0.254
Epoch: 49 -> Loss: 0.238557979465
Epoch: 49 -> Test Accuracy: 83.07
[50, 60] loss: 0.237
[50, 120] loss: 0.234
[50, 180] loss: 0.242
[50, 240] loss: 0.237
[50, 300] loss: 0.238
[50, 360] loss: 0.246
Epoch: 50 -> Loss: 0.215908616781
Epoch: 50 -> Test Accuracy: 83.12
[51, 60] loss: 0.224
[51, 120] loss: 0.231
[51, 180] loss: 0.227
[51, 240] loss: 0.252
[51, 300] loss: 0.251
[51, 360] loss: 0.254
Epoch: 51 -> Loss: 0.225903347135
Epoch: 51 -> Test Accuracy: 83.26
[52, 60] loss: 0.240
[52, 120] loss: 0.236
[52, 180] loss: 0.251
[52, 240] loss: 0.240
[52, 300] loss: 0.254
[52, 360] loss: 0.255
Epoch: 52 -> Loss: 0.3186224401
Epoch: 52 -> Test Accuracy: 83.42
[53, 60] loss: 0.238
[53, 120] loss: 0.234
[53, 180] loss: 0.223
[53, 240] loss: 0.239
[53, 300] loss: 0.250
[53, 360] loss: 0.252
Epoch: 53 -> Loss: 0.212616443634
Epoch: 53 -> Test Accuracy: 83.59
[54, 60] loss: 0.214
[54, 120] loss: 0.242
[54, 180] loss: 0.236
[54, 240] loss: 0.240
[54, 300] loss: 0.238
[54, 360] loss: 0.250
Epoch: 54 -> Loss: 0.227939888835
Epoch: 54 -> Test Accuracy: 83.62
[55, 60] loss: 0.233
[55, 120] loss: 0.234
[55, 180] loss: 0.244
[55, 240] loss: 0.246
[55, 300] loss: 0.247
[55, 360] loss: 0.239
Epoch: 55 -> Loss: 0.247250676155
Epoch: 55 -> Test Accuracy: 82.97
[56, 60] loss: 0.221
[56, 120] loss: 0.232
[56, 180] loss: 0.229
[56, 240] loss: 0.231
[56, 300] loss: 0.258
[56, 360] loss: 0.242
Epoch: 56 -> Loss: 0.195344880223
Epoch: 56 -> Test Accuracy: 83.28
[57, 60] loss: 0.223
[57, 120] loss: 0.233
[57, 180] loss: 0.238
[57, 240] loss: 0.226
[57, 300] loss: 0.234
[57, 360] loss: 0.241
Epoch: 57 -> Loss: 0.224278539419
Epoch: 57 -> Test Accuracy: 83.27
[58, 60] loss: 0.222
[58, 120] loss: 0.241
[58, 180] loss: 0.245
[58, 240] loss: 0.232
[58, 300] loss: 0.232
[58, 360] loss: 0.252
Epoch: 58 -> Loss: 0.162653848529
Epoch: 58 -> Test Accuracy: 83.1
[59, 60] loss: 0.228
[59, 120] loss: 0.232
[59, 180] loss: 0.224
[59, 240] loss: 0.233
[59, 300] loss: 0.237
[59, 360] loss: 0.235
Epoch: 59 -> Loss: 0.427598625422
Epoch: 59 -> Test Accuracy: 83.11
[60, 60] loss: 0.222
[60, 120] loss: 0.219
[60, 180] loss: 0.222
[60, 240] loss: 0.238
[60, 300] loss: 0.229
[60, 360] loss: 0.247
Epoch: 60 -> Loss: 0.244497850537
Epoch: 60 -> Test Accuracy: 82.93
[61, 60] loss: 0.220
[61, 120] loss: 0.213
[61, 180] loss: 0.231
[61, 240] loss: 0.219
[61, 300] loss: 0.240
[61, 360] loss: 0.247
Epoch: 61 -> Loss: 0.191278129816
Epoch: 61 -> Test Accuracy: 83.73
[62, 60] loss: 0.226
[62, 120] loss: 0.228
[62, 180] loss: 0.228
[62, 240] loss: 0.240
[62, 300] loss: 0.230
[62, 360] loss: 0.238
Epoch: 62 -> Loss: 0.288082003593
Epoch: 62 -> Test Accuracy: 83.21
[63, 60] loss: 0.216
[63, 120] loss: 0.223
[63, 180] loss: 0.226
[63, 240] loss: 0.244
[63, 300] loss: 0.229
[63, 360] loss: 0.233
Epoch: 63 -> Loss: 0.312612712383
Epoch: 63 -> Test Accuracy: 83.28
[64, 60] loss: 0.216
[64, 120] loss: 0.229
[64, 180] loss: 0.226
[64, 240] loss: 0.221
[64, 300] loss: 0.252
[64, 360] loss: 0.234
Epoch: 64 -> Loss: 0.145518258214
Epoch: 64 -> Test Accuracy: 83.6
[65, 60] loss: 0.219
[65, 120] loss: 0.222
[65, 180] loss: 0.230
[65, 240] loss: 0.236
[65, 300] loss: 0.229
[65, 360] loss: 0.224
Epoch: 65 -> Loss: 0.250201255083
Epoch: 65 -> Test Accuracy: 83.16
[66, 60] loss: 0.227
[66, 120] loss: 0.209
[66, 180] loss: 0.228
[66, 240] loss: 0.235
[66, 300] loss: 0.230
[66, 360] loss: 0.230
Epoch: 66 -> Loss: 0.285742133856
Epoch: 66 -> Test Accuracy: 83.06
[67, 60] loss: 0.224
[67, 120] loss: 0.219
[67, 180] loss: 0.230
[67, 240] loss: 0.227
[67, 300] loss: 0.234
[67, 360] loss: 0.235
Epoch: 67 -> Loss: 0.169107034802
Epoch: 67 -> Test Accuracy: 83.19
[68, 60] loss: 0.214
[68, 120] loss: 0.224
[68, 180] loss: 0.236
[68, 240] loss: 0.237
[68, 300] loss: 0.234
[68, 360] loss: 0.230
Epoch: 68 -> Loss: 0.157448172569
Epoch: 68 -> Test Accuracy: 83.38
[69, 60] loss: 0.207
[69, 120] loss: 0.205
[69, 180] loss: 0.228
[69, 240] loss: 0.223
[69, 300] loss: 0.241
[69, 360] loss: 0.229
Epoch: 69 -> Loss: 0.102319121361
Epoch: 69 -> Test Accuracy: 82.27
[70, 60] loss: 0.206
[70, 120] loss: 0.219
[70, 180] loss: 0.229
[70, 240] loss: 0.229
[70, 300] loss: 0.241
[70, 360] loss: 0.238
Epoch: 70 -> Loss: 0.20478323102
Epoch: 70 -> Test Accuracy: 82.68
[71, 60] loss: 0.202
[71, 120] loss: 0.178
[71, 180] loss: 0.174
[71, 240] loss: 0.169
[71, 300] loss: 0.166
[71, 360] loss: 0.162
Epoch: 71 -> Loss: 0.15055783093
Epoch: 71 -> Test Accuracy: 84.06
[72, 60] loss: 0.154
[72, 120] loss: 0.168
[72, 180] loss: 0.160
[72, 240] loss: 0.163
[72, 300] loss: 0.163
[72, 360] loss: 0.158
Epoch: 72 -> Loss: 0.249161928892
Epoch: 72 -> Test Accuracy: 84.42
[73, 60] loss: 0.152
[73, 120] loss: 0.153
[73, 180] loss: 0.147
[73, 240] loss: 0.151
[73, 300] loss: 0.143
[73, 360] loss: 0.156
Epoch: 73 -> Loss: 0.0970067381859
Epoch: 73 -> Test Accuracy: 84.54
[74, 60] loss: 0.147
[74, 120] loss: 0.139
[74, 180] loss: 0.145
[74, 240] loss: 0.162
[74, 300] loss: 0.153
[74, 360] loss: 0.141
Epoch: 74 -> Loss: 0.22745513916
Epoch: 74 -> Test Accuracy: 84.55
[75, 60] loss: 0.147
[75, 120] loss: 0.139
[75, 180] loss: 0.143
[75, 240] loss: 0.146
[75, 300] loss: 0.143
[75, 360] loss: 0.148
Epoch: 75 -> Loss: 0.170565709472
Epoch: 75 -> Test Accuracy: 84.31
[76, 60] loss: 0.146
[76, 120] loss: 0.140
[76, 180] loss: 0.142
[76, 240] loss: 0.138
[76, 300] loss: 0.146
[76, 360] loss: 0.139
Epoch: 76 -> Loss: 0.113970793784
Epoch: 76 -> Test Accuracy: 84.28
[77, 60] loss: 0.134
[77, 120] loss: 0.134
[77, 180] loss: 0.143
[77, 240] loss: 0.142
[77, 300] loss: 0.133
[77, 360] loss: 0.141
Epoch: 77 -> Loss: 0.0753377526999
Epoch: 77 -> Test Accuracy: 84.55
[78, 60] loss: 0.138
[78, 120] loss: 0.137
[78, 180] loss: 0.131
[78, 240] loss: 0.133
[78, 300] loss: 0.135
[78, 360] loss: 0.136
Epoch: 78 -> Loss: 0.303553342819
Epoch: 78 -> Test Accuracy: 84.35
[79, 60] loss: 0.129
[79, 120] loss: 0.130
[79, 180] loss: 0.130
[79, 240] loss: 0.134
[79, 300] loss: 0.135
[79, 360] loss: 0.125
Epoch: 79 -> Loss: 0.205618694425
Epoch: 79 -> Test Accuracy: 83.98
[80, 60] loss: 0.131
[80, 120] loss: 0.123
[80, 180] loss: 0.132
[80, 240] loss: 0.124
[80, 300] loss: 0.131
[80, 360] loss: 0.138
Epoch: 80 -> Loss: 0.188370659947
Epoch: 80 -> Test Accuracy: 84.16
[81, 60] loss: 0.126
[81, 120] loss: 0.136
[81, 180] loss: 0.130
[81, 240] loss: 0.129
[81, 300] loss: 0.118
[81, 360] loss: 0.131
Epoch: 81 -> Loss: 0.0754401236773
Epoch: 81 -> Test Accuracy: 84.42
[82, 60] loss: 0.123
[82, 120] loss: 0.121
[82, 180] loss: 0.127
[82, 240] loss: 0.134
[82, 300] loss: 0.129
[82, 360] loss: 0.129
Epoch: 82 -> Loss: 0.121076270938
Epoch: 82 -> Test Accuracy: 84.02
[83, 60] loss: 0.124
[83, 120] loss: 0.120
[83, 180] loss: 0.121
[83, 240] loss: 0.133
[83, 300] loss: 0.131
[83, 360] loss: 0.130
Epoch: 83 -> Loss: 0.229584142566
Epoch: 83 -> Test Accuracy: 83.99
[84, 60] loss: 0.115
[84, 120] loss: 0.126
[84, 180] loss: 0.125
[84, 240] loss: 0.116
[84, 300] loss: 0.118
[84, 360] loss: 0.124
Epoch: 84 -> Loss: 0.100957512856
Epoch: 84 -> Test Accuracy: 84.02
[85, 60] loss: 0.119
[85, 120] loss: 0.120
[85, 180] loss: 0.122
[85, 240] loss: 0.127
[85, 300] loss: 0.135
[85, 360] loss: 0.122
Epoch: 85 -> Loss: 0.131534129381
Epoch: 85 -> Test Accuracy: 83.75
[86, 60] loss: 0.116
[86, 120] loss: 0.105
[86, 180] loss: 0.107
[86, 240] loss: 0.115
[86, 300] loss: 0.111
[86, 360] loss: 0.111
Epoch: 86 -> Loss: 0.100786723197
Epoch: 86 -> Test Accuracy: 84.13
[87, 60] loss: 0.100
[87, 120] loss: 0.110
[87, 180] loss: 0.108
[87, 240] loss: 0.112
[87, 300] loss: 0.107
[87, 360] loss: 0.105
Epoch: 87 -> Loss: 0.144306018949
Epoch: 87 -> Test Accuracy: 84.2
[88, 60] loss: 0.105
[88, 120] loss: 0.104
[88, 180] loss: 0.106
[88, 240] loss: 0.100
[88, 300] loss: 0.107
[88, 360] loss: 0.107
Epoch: 88 -> Loss: 0.0612943395972
Epoch: 88 -> Test Accuracy: 84.24
[89, 60] loss: 0.111
[89, 120] loss: 0.104
[89, 180] loss: 0.104
[89, 240] loss: 0.102
[89, 300] loss: 0.100
[89, 360] loss: 0.110
Epoch: 89 -> Loss: 0.154033973813
Epoch: 89 -> Test Accuracy: 84.23
[90, 60] loss: 0.098
[90, 120] loss: 0.102
[90, 180] loss: 0.105
[90, 240] loss: 0.105
[90, 300] loss: 0.108
[90, 360] loss: 0.100
Epoch: 90 -> Loss: 0.146867543459
Epoch: 90 -> Test Accuracy: 84.18
[91, 60] loss: 0.101
[91, 120] loss: 0.100
[91, 180] loss: 0.106
[91, 240] loss: 0.109
[91, 300] loss: 0.100
[91, 360] loss: 0.098
Epoch: 91 -> Loss: 0.0843537226319
Epoch: 91 -> Test Accuracy: 84.15
[92, 60] loss: 0.101
[92, 120] loss: 0.097
[92, 180] loss: 0.104
[92, 240] loss: 0.097
[92, 300] loss: 0.104
[92, 360] loss: 0.104
Epoch: 92 -> Loss: 0.10631134361
Epoch: 92 -> Test Accuracy: 84.08
[93, 60] loss: 0.108
[93, 120] loss: 0.098
[93, 180] loss: 0.104
[93, 240] loss: 0.098
[93, 300] loss: 0.102
[93, 360] loss: 0.103
Epoch: 93 -> Loss: 0.0924872383475
Epoch: 93 -> Test Accuracy: 84.11
[94, 60] loss: 0.101
[94, 120] loss: 0.100
[94, 180] loss: 0.100
[94, 240] loss: 0.102
[94, 300] loss: 0.104
[94, 360] loss: 0.102
Epoch: 94 -> Loss: 0.139544397593
Epoch: 94 -> Test Accuracy: 84.28
[95, 60] loss: 0.099
[95, 120] loss: 0.101
[95, 180] loss: 0.098
[95, 240] loss: 0.102
[95, 300] loss: 0.095
[95, 360] loss: 0.099
Epoch: 95 -> Loss: 0.10687482357
Epoch: 95 -> Test Accuracy: 84.29
[96, 60] loss: 0.097
[96, 120] loss: 0.098
[96, 180] loss: 0.099
[96, 240] loss: 0.103
[96, 300] loss: 0.107
[96, 360] loss: 0.099
Epoch: 96 -> Loss: 0.0943318530917
Epoch: 96 -> Test Accuracy: 84.13
[97, 60] loss: 0.098
[97, 120] loss: 0.097
[97, 180] loss: 0.100
[97, 240] loss: 0.100
[97, 300] loss: 0.102
[97, 360] loss: 0.107
Epoch: 97 -> Loss: 0.070753082633
Epoch: 97 -> Test Accuracy: 84.34
[98, 60] loss: 0.102
[98, 120] loss: 0.097
[98, 180] loss: 0.102
[98, 240] loss: 0.094
[98, 300] loss: 0.096
[98, 360] loss: 0.097
Epoch: 98 -> Loss: 0.124475643039
Epoch: 98 -> Test Accuracy: 84.35
[99, 60] loss: 0.098
[99, 120] loss: 0.092
[99, 180] loss: 0.097
[99, 240] loss: 0.100
[99, 300] loss: 0.105
[99, 360] loss: 0.095
Epoch: 99 -> Loss: 0.135437041521
Epoch: 99 -> Test Accuracy: 84.19
[100, 60] loss: 0.096
[100, 120] loss: 0.092
[100, 180] loss: 0.094
[100, 240] loss: 0.109
[100, 300] loss: 0.099
[100, 360] loss: 0.101
Epoch: 100 -> Loss: 0.0642497316003
Epoch: 100 -> Test Accuracy: 84.25
Finished Training
[1, 60] loss: 2.078
[1, 120] loss: 1.888
[1, 180] loss: 1.832
[1, 240] loss: 1.765
[1, 300] loss: 1.739
[1, 360] loss: 1.707
Epoch: 1 -> Loss: 1.54911959171
Epoch: 1 -> Test Accuracy: 33.97
[2, 60] loss: 1.692
[2, 120] loss: 1.663
[2, 180] loss: 1.658
[2, 240] loss: 1.640
[2, 300] loss: 1.648
[2, 360] loss: 1.617
Epoch: 2 -> Loss: 1.53013265133
Epoch: 2 -> Test Accuracy: 36.23
[3, 60] loss: 1.605
[3, 120] loss: 1.612
[3, 180] loss: 1.589
[3, 240] loss: 1.597
[3, 300] loss: 1.578
[3, 360] loss: 1.587
Epoch: 3 -> Loss: 1.47031855583
Epoch: 3 -> Test Accuracy: 38.1
[4, 60] loss: 1.544
[4, 120] loss: 1.563
[4, 180] loss: 1.591
[4, 240] loss: 1.556
[4, 300] loss: 1.541
[4, 360] loss: 1.572
Epoch: 4 -> Loss: 1.65348505974
Epoch: 4 -> Test Accuracy: 38.78
[5, 60] loss: 1.549
[5, 120] loss: 1.554
[5, 180] loss: 1.534
[5, 240] loss: 1.538
[5, 300] loss: 1.542
[5, 360] loss: 1.542
Epoch: 5 -> Loss: 1.35668408871
Epoch: 5 -> Test Accuracy: 40.06
[6, 60] loss: 1.528
[6, 120] loss: 1.515
[6, 180] loss: 1.525
[6, 240] loss: 1.531
[6, 300] loss: 1.526
[6, 360] loss: 1.512
Epoch: 6 -> Loss: 1.47895634174
Epoch: 6 -> Test Accuracy: 39.17
[7, 60] loss: 1.527
[7, 120] loss: 1.500
[7, 180] loss: 1.509
[7, 240] loss: 1.519
[7, 300] loss: 1.486
[7, 360] loss: 1.522
Epoch: 7 -> Loss: 1.44632053375
Epoch: 7 -> Test Accuracy: 40.48
[8, 60] loss: 1.494
[8, 120] loss: 1.527
[8, 180] loss: 1.483
[8, 240] loss: 1.484
[8, 300] loss: 1.505
[8, 360] loss: 1.493
Epoch: 8 -> Loss: 1.57093656063
Epoch: 8 -> Test Accuracy: 41.27
[9, 60] loss: 1.490
[9, 120] loss: 1.496
[9, 180] loss: 1.491
[9, 240] loss: 1.465
[9, 300] loss: 1.497
[9, 360] loss: 1.488
Epoch: 9 -> Loss: 1.37853515148
Epoch: 9 -> Test Accuracy: 40.99
[10, 60] loss: 1.471
[10, 120] loss: 1.467
[10, 180] loss: 1.497
[10, 240] loss: 1.494
[10, 300] loss: 1.482
[10, 360] loss: 1.502
Epoch: 10 -> Loss: 1.41953253746
Epoch: 10 -> Test Accuracy: 39.73
[11, 60] loss: 1.498
[11, 120] loss: 1.468
[11, 180] loss: 1.479
[11, 240] loss: 1.478
[11, 300] loss: 1.500
[11, 360] loss: 1.478
Epoch: 11 -> Loss: 1.52441179752
Epoch: 11 -> Test Accuracy: 39.65
[12, 60] loss: 1.487
[12, 120] loss: 1.482
[12, 180] loss: 1.476
[12, 240] loss: 1.470
[12, 300] loss: 1.499
[12, 360] loss: 1.471
Epoch: 12 -> Loss: 1.48645138741
Epoch: 12 -> Test Accuracy: 40.45
[13, 60] loss: 1.463
[13, 120] loss: 1.470
[13, 180] loss: 1.474
[13, 240] loss: 1.462
[13, 300] loss: 1.461
[13, 360] loss: 1.486
Epoch: 13 -> Loss: 1.38837504387
Epoch: 13 -> Test Accuracy: 41.59
[14, 60] loss: 1.482
[14, 120] loss: 1.472
[14, 180] loss: 1.468
[14, 240] loss: 1.479
[14, 300] loss: 1.464
[14, 360] loss: 1.469
Epoch: 14 -> Loss: 1.35015308857
Epoch: 14 -> Test Accuracy: 41.25
[15, 60] loss: 1.440
[15, 120] loss: 1.471
[15, 180] loss: 1.472
[15, 240] loss: 1.470
[15, 300] loss: 1.455
[15, 360] loss: 1.469
Epoch: 15 -> Loss: 1.38466477394
Epoch: 15 -> Test Accuracy: 41.58
[16, 60] loss: 1.464
[16, 120] loss: 1.478
[16, 180] loss: 1.457
[16, 240] loss: 1.459
[16, 300] loss: 1.434
[16, 360] loss: 1.446
Epoch: 16 -> Loss: 1.63905179501
Epoch: 16 -> Test Accuracy: 41.82
[17, 60] loss: 1.464
[17, 120] loss: 1.460
[17, 180] loss: 1.462
[17, 240] loss: 1.451
[17, 300] loss: 1.453
[17, 360] loss: 1.461
Epoch: 17 -> Loss: 1.62019217014
Epoch: 17 -> Test Accuracy: 41.56
[18, 60] loss: 1.470
[18, 120] loss: 1.439
[18, 180] loss: 1.446
[18, 240] loss: 1.465
[18, 300] loss: 1.463
[18, 360] loss: 1.432
Epoch: 18 -> Loss: 1.51963615417
Epoch: 18 -> Test Accuracy: 41.77
[19, 60] loss: 1.456
[19, 120] loss: 1.456
[19, 180] loss: 1.453
[19, 240] loss: 1.433
[19, 300] loss: 1.474
[19, 360] loss: 1.429
Epoch: 19 -> Loss: 1.47823345661
Epoch: 19 -> Test Accuracy: 41.85
[20, 60] loss: 1.439
[20, 120] loss: 1.451
[20, 180] loss: 1.448
[20, 240] loss: 1.433
[20, 300] loss: 1.445
[20, 360] loss: 1.458
Epoch: 20 -> Loss: 1.52669274807
Epoch: 20 -> Test Accuracy: 42.79
[21, 60] loss: 1.464
[21, 120] loss: 1.454
[21, 180] loss: 1.454
[21, 240] loss: 1.435
[21, 300] loss: 1.423
[21, 360] loss: 1.436
Epoch: 21 -> Loss: 1.40968728065
Epoch: 21 -> Test Accuracy: 42.19
[22, 60] loss: 1.437
[22, 120] loss: 1.442
[22, 180] loss: 1.455
[22, 240] loss: 1.460
[22, 300] loss: 1.466
[22, 360] loss: 1.442
Epoch: 22 -> Loss: 1.40106499195
Epoch: 22 -> Test Accuracy: 42.37
[23, 60] loss: 1.442
[23, 120] loss: 1.451
[23, 180] loss: 1.451
[23, 240] loss: 1.453
[23, 300] loss: 1.448
[23, 360] loss: 1.447
Epoch: 23 -> Loss: 1.31810796261
Epoch: 23 -> Test Accuracy: 42.31
[24, 60] loss: 1.470
[24, 120] loss: 1.437
[24, 180] loss: 1.446
[24, 240] loss: 1.442
[24, 300] loss: 1.452
[24, 360] loss: 1.458
Epoch: 24 -> Loss: 1.42444729805
Epoch: 24 -> Test Accuracy: 42.04
[25, 60] loss: 1.452
[25, 120] loss: 1.431
[25, 180] loss: 1.427
[25, 240] loss: 1.443
[25, 300] loss: 1.435
[25, 360] loss: 1.453
Epoch: 25 -> Loss: 1.44548606873
Epoch: 25 -> Test Accuracy: 40.63
[26, 60] loss: 1.440
[26, 120] loss: 1.433
[26, 180] loss: 1.449
[26, 240] loss: 1.442
[26, 300] loss: 1.451
[26, 360] loss: 1.449
Epoch: 26 -> Loss: 1.42789113522
Epoch: 26 -> Test Accuracy: 39.72
[27, 60] loss: 1.444
[27, 120] loss: 1.433
[27, 180] loss: 1.447
[27, 240] loss: 1.450
[27, 300] loss: 1.445
[27, 360] loss: 1.439
Epoch: 27 -> Loss: 1.29797279835
Epoch: 27 -> Test Accuracy: 42.67
[28, 60] loss: 1.452
[28, 120] loss: 1.436
[28, 180] loss: 1.443
[28, 240] loss: 1.457
[28, 300] loss: 1.438
[28, 360] loss: 1.435
Epoch: 28 -> Loss: 1.34364151955
Epoch: 28 -> Test Accuracy: 42.38
[29, 60] loss: 1.440
[29, 120] loss: 1.450
[29, 180] loss: 1.427
[29, 240] loss: 1.448
[29, 300] loss: 1.449
[29, 360] loss: 1.436
Epoch: 29 -> Loss: 1.34085488319
Epoch: 29 -> Test Accuracy: 40.93
[30, 60] loss: 1.413
[30, 120] loss: 1.448
[30, 180] loss: 1.446
[30, 240] loss: 1.458
[30, 300] loss: 1.430
[30, 360] loss: 1.444
Epoch: 30 -> Loss: 1.22373712063
Epoch: 30 -> Test Accuracy: 41.05
[31, 60] loss: 1.471
[31, 120] loss: 1.432
[31, 180] loss: 1.433
[31, 240] loss: 1.435
[31, 300] loss: 1.441
[31, 360] loss: 1.447
Epoch: 31 -> Loss: 1.49939751625
Epoch: 31 -> Test Accuracy: 42.81
[32, 60] loss: 1.445
[32, 120] loss: 1.422
[32, 180] loss: 1.428
[32, 240] loss: 1.455
[32, 300] loss: 1.459
[32, 360] loss: 1.438
Epoch: 32 -> Loss: 1.27505433559
Epoch: 32 -> Test Accuracy: 42.34
[33, 60] loss: 1.420
[33, 120] loss: 1.430
[33, 180] loss: 1.451
[33, 240] loss: 1.440
[33, 300] loss: 1.441
[33, 360] loss: 1.432
Epoch: 33 -> Loss: 1.40621566772
Epoch: 33 -> Test Accuracy: 42.84
[34, 60] loss: 1.443
[34, 120] loss: 1.443
[34, 180] loss: 1.428
[34, 240] loss: 1.436
[34, 300] loss: 1.445
[34, 360] loss: 1.435
Epoch: 34 -> Loss: 1.59400904179
Epoch: 34 -> Test Accuracy: 43.24
[35, 60] loss: 1.419
[35, 120] loss: 1.445
[35, 180] loss: 1.447
[35, 240] loss: 1.453
[35, 300] loss: 1.419
[35, 360] loss: 1.442
Epoch: 35 -> Loss: 1.48838484287
Epoch: 35 -> Test Accuracy: 41.14
[36, 60] loss: 1.346
[36, 120] loss: 1.326
[36, 180] loss: 1.339
[36, 240] loss: 1.338
[36, 300] loss: 1.318
[36, 360] loss: 1.316
Epoch: 36 -> Loss: 1.19623494148
Epoch: 36 -> Test Accuracy: 46.66
[37, 60] loss: 1.321
[37, 120] loss: 1.319
[37, 180] loss: 1.294
[37, 240] loss: 1.305
[37, 300] loss: 1.298
[37, 360] loss: 1.305
Epoch: 37 -> Loss: 1.50831675529
Epoch: 37 -> Test Accuracy: 46.22
[38, 60] loss: 1.312
[38, 120] loss: 1.264
[38, 180] loss: 1.304
[38, 240] loss: 1.297
[38, 300] loss: 1.296
[38, 360] loss: 1.306
Epoch: 38 -> Loss: 1.43125188351
Epoch: 38 -> Test Accuracy: 46.48
[39, 60] loss: 1.302
[39, 120] loss: 1.288
[39, 180] loss: 1.285
[39, 240] loss: 1.290
[39, 300] loss: 1.305
[39, 360] loss: 1.319
Epoch: 39 -> Loss: 1.29466211796
Epoch: 39 -> Test Accuracy: 47.21
[40, 60] loss: 1.303
[40, 120] loss: 1.295
[40, 180] loss: 1.282
[40, 240] loss: 1.290
[40, 300] loss: 1.294
[40, 360] loss: 1.298
Epoch: 40 -> Loss: 1.35837340355
Epoch: 40 -> Test Accuracy: 47.26
[41, 60] loss: 1.294
[41, 120] loss: 1.278
[41, 180] loss: 1.316
[41, 240] loss: 1.281
[41, 300] loss: 1.290
[41, 360] loss: 1.282
Epoch: 41 -> Loss: 1.26080310345
Epoch: 41 -> Test Accuracy: 47.14
[42, 60] loss: 1.275
[42, 120] loss: 1.296
[42, 180] loss: 1.292
[42, 240] loss: 1.284
[42, 300] loss: 1.302
[42, 360] loss: 1.283
Epoch: 42 -> Loss: 1.25724124908
Epoch: 42 -> Test Accuracy: 46.73
[43, 60] loss: 1.286
[43, 120] loss: 1.299
[43, 180] loss: 1.291
[43, 240] loss: 1.283
[43, 300] loss: 1.287
[43, 360] loss: 1.285
Epoch: 43 -> Loss: 1.5090290308
Epoch: 43 -> Test Accuracy: 46.82
[44, 60] loss: 1.291
[44, 120] loss: 1.299
[44, 180] loss: 1.292
[44, 240] loss: 1.291
[44, 300] loss: 1.276
[44, 360] loss: 1.277
Epoch: 44 -> Loss: 1.28428435326
Epoch: 44 -> Test Accuracy: 47.33
[45, 60] loss: 1.296
[45, 120] loss: 1.284
[45, 180] loss: 1.284
[45, 240] loss: 1.287
[45, 300] loss: 1.299
[45, 360] loss: 1.277
Epoch: 45 -> Loss: 1.26648044586
Epoch: 45 -> Test Accuracy: 47.16
[46, 60] loss: 1.290
[46, 120] loss: 1.269
[46, 180] loss: 1.272
[46, 240] loss: 1.295
[46, 300] loss: 1.311
[46, 360] loss: 1.294
Epoch: 46 -> Loss: 1.4980442524
Epoch: 46 -> Test Accuracy: 47.08
[47, 60] loss: 1.280
[47, 120] loss: 1.282
[47, 180] loss: 1.312
[47, 240] loss: 1.274
[47, 300] loss: 1.272
[47, 360] loss: 1.308
Epoch: 47 -> Loss: 1.35201942921
Epoch: 47 -> Test Accuracy: 46.76
[48, 60] loss: 1.300
[48, 120] loss: 1.277
[48, 180] loss: 1.279
[48, 240] loss: 1.296
[48, 300] loss: 1.269
[48, 360] loss: 1.306
Epoch: 48 -> Loss: 1.312510252
Epoch: 48 -> Test Accuracy: 46.17
[49, 60] loss: 1.277
[49, 120] loss: 1.296
[49, 180] loss: 1.277
[49, 240] loss: 1.282
[49, 300] loss: 1.292
[49, 360] loss: 1.282
Epoch: 49 -> Loss: 1.38111102581
Epoch: 49 -> Test Accuracy: 47.61
[50, 60] loss: 1.273
[50, 120] loss: 1.285
[50, 180] loss: 1.288
[50, 240] loss: 1.300
[50, 300] loss: 1.279
[50, 360] loss: 1.286
Epoch: 50 -> Loss: 1.38592135906
Epoch: 50 -> Test Accuracy: 46.45
[51, 60] loss: 1.285
[51, 120] loss: 1.294
[51, 180] loss: 1.278
[51, 240] loss: 1.279
[51, 300] loss: 1.274
[51, 360] loss: 1.285
Epoch: 51 -> Loss: 1.33155751228
Epoch: 51 -> Test Accuracy: 46.81
[52, 60] loss: 1.274
[52, 120] loss: 1.285
[52, 180] loss: 1.291
[52, 240] loss: 1.305
[52, 300] loss: 1.284
[52, 360] loss: 1.281
Epoch: 52 -> Loss: 1.2719591856
Epoch: 52 -> Test Accuracy: 48.22
[53, 60] loss: 1.280
[53, 120] loss: 1.274
[53, 180] loss: 1.293
[53, 240] loss: 1.302
[53, 300] loss: 1.278
[53, 360] loss: 1.288
Epoch: 53 -> Loss: 1.25599789619
Epoch: 53 -> Test Accuracy: 47.8
[54, 60] loss: 1.308
[54, 120] loss: 1.283
[54, 180] loss: 1.294
[54, 240] loss: 1.278
[54, 300] loss: 1.274
[54, 360] loss: 1.286
Epoch: 54 -> Loss: 1.26343917847
Epoch: 54 -> Test Accuracy: 47.52
[55, 60] loss: 1.272
[55, 120] loss: 1.265
[55, 180] loss: 1.291
[55, 240] loss: 1.296
[55, 300] loss: 1.307
[55, 360] loss: 1.282
Epoch: 55 -> Loss: 1.24955439568
Epoch: 55 -> Test Accuracy: 47.03
[56, 60] loss: 1.287
[56, 120] loss: 1.286
[56, 180] loss: 1.286
[56, 240] loss: 1.282
[56, 300] loss: 1.275
[56, 360] loss: 1.288
Epoch: 56 -> Loss: 1.33808410168
Epoch: 56 -> Test Accuracy: 47.26
[57, 60] loss: 1.283
[57, 120] loss: 1.288
[57, 180] loss: 1.284
[57, 240] loss: 1.289
[57, 300] loss: 1.279
[57, 360] loss: 1.277
Epoch: 57 -> Loss: 1.42024540901
Epoch: 57 -> Test Accuracy: 46.89
[58, 60] loss: 1.262
[58, 120] loss: 1.282
[58, 180] loss: 1.284
[58, 240] loss: 1.280
[58, 300] loss: 1.292
[58, 360] loss: 1.311
Epoch: 58 -> Loss: 1.24104607105
Epoch: 58 -> Test Accuracy: 48.01
[59, 60] loss: 1.296
[59, 120] loss: 1.275
[59, 180] loss: 1.273
[59, 240] loss: 1.274
[59, 300] loss: 1.285
[59, 360] loss: 1.269
Epoch: 59 -> Loss: 1.39220154285
Epoch: 59 -> Test Accuracy: 47.22
[60, 60] loss: 1.291
[60, 120] loss: 1.288
[60, 180] loss: 1.297
[60, 240] loss: 1.274
[60, 300] loss: 1.278
[60, 360] loss: 1.281
Epoch: 60 -> Loss: 1.36200547218
Epoch: 60 -> Test Accuracy: 47.83
[61, 60] loss: 1.276
[61, 120] loss: 1.274
[61, 180] loss: 1.283
[61, 240] loss: 1.294
[61, 300] loss: 1.281
[61, 360] loss: 1.280
Epoch: 61 -> Loss: 1.38666749001
Epoch: 61 -> Test Accuracy: 47.69
[62, 60] loss: 1.257
[62, 120] loss: 1.298
[62, 180] loss: 1.288
[62, 240] loss: 1.279
[62, 300] loss: 1.297
[62, 360] loss: 1.287
Epoch: 62 -> Loss: 1.23714363575
Epoch: 62 -> Test Accuracy: 46.32
[63, 60] loss: 1.282
[63, 120] loss: 1.264
[63, 180] loss: 1.283
[63, 240] loss: 1.287
[63, 300] loss: 1.282
[63, 360] loss: 1.278
Epoch: 63 -> Loss: 1.41803729534
Epoch: 63 -> Test Accuracy: 47.59
[64, 60] loss: 1.267
[64, 120] loss: 1.281
[64, 180] loss: 1.285
[64, 240] loss: 1.276
[64, 300] loss: 1.283
[64, 360] loss: 1.279
Epoch: 64 -> Loss: 1.23836088181
Epoch: 64 -> Test Accuracy: 47.52
[65, 60] loss: 1.270
[65, 120] loss: 1.271
[65, 180] loss: 1.285
[65, 240] loss: 1.280
[65, 300] loss: 1.293
[65, 360] loss: 1.288
Epoch: 65 -> Loss: 1.40561938286
Epoch: 65 -> Test Accuracy: 46.57
[66, 60] loss: 1.276
[66, 120] loss: 1.266
[66, 180] loss: 1.275
[66, 240] loss: 1.268
[66, 300] loss: 1.269
[66, 360] loss: 1.304
Epoch: 66 -> Loss: 1.28430604935
Epoch: 66 -> Test Accuracy: 47.95
[67, 60] loss: 1.277
[67, 120] loss: 1.267
[67, 180] loss: 1.270
[67, 240] loss: 1.283
[67, 300] loss: 1.280
[67, 360] loss: 1.275
Epoch: 67 -> Loss: 1.29519677162
Epoch: 67 -> Test Accuracy: 47.15
[68, 60] loss: 1.275
[68, 120] loss: 1.279
[68, 180] loss: 1.289
[68, 240] loss: 1.279
[68, 300] loss: 1.282
[68, 360] loss: 1.288
Epoch: 68 -> Loss: 1.24993872643
Epoch: 68 -> Test Accuracy: 46.99
[69, 60] loss: 1.265
[69, 120] loss: 1.267
[69, 180] loss: 1.260
[69, 240] loss: 1.272
[69, 300] loss: 1.258
[69, 360] loss: 1.281
Epoch: 69 -> Loss: 1.31426775455
Epoch: 69 -> Test Accuracy: 48.25
[70, 60] loss: 1.282
[70, 120] loss: 1.254
[70, 180] loss: 1.268
[70, 240] loss: 1.288
[70, 300] loss: 1.273
[70, 360] loss: 1.280
Epoch: 70 -> Loss: 1.25288391113
Epoch: 70 -> Test Accuracy: 48.14
[71, 60] loss: 1.203
[71, 120] loss: 1.212
[71, 180] loss: 1.209
[71, 240] loss: 1.200
[71, 300] loss: 1.215
[71, 360] loss: 1.181
Epoch: 71 -> Loss: 0.996318817139
Epoch: 71 -> Test Accuracy: 50.5
[72, 60] loss: 1.186
[72, 120] loss: 1.198
[72, 180] loss: 1.187
[72, 240] loss: 1.189
[72, 300] loss: 1.175
[72, 360] loss: 1.185
Epoch: 72 -> Loss: 1.13805437088
Epoch: 72 -> Test Accuracy: 50.65
[73, 60] loss: 1.170
[73, 120] loss: 1.182
[73, 180] loss: 1.175
[73, 240] loss: 1.195
[73, 300] loss: 1.208
[73, 360] loss: 1.180
Epoch: 73 -> Loss: 1.21059799194
Epoch: 73 -> Test Accuracy: 51.22
[74, 60] loss: 1.161
[74, 120] loss: 1.167
[74, 180] loss: 1.153
[74, 240] loss: 1.182
[74, 300] loss: 1.168
[74, 360] loss: 1.169
Epoch: 74 -> Loss: 1.24865567684
Epoch: 74 -> Test Accuracy: 51.04
[75, 60] loss: 1.159
[75, 120] loss: 1.161
[75, 180] loss: 1.161
[75, 240] loss: 1.162
[75, 300] loss: 1.191
[75, 360] loss: 1.172
Epoch: 75 -> Loss: 1.15342116356
Epoch: 75 -> Test Accuracy: 51.09
[76, 60] loss: 1.170
[76, 120] loss: 1.179
[76, 180] loss: 1.179
[76, 240] loss: 1.183
[76, 300] loss: 1.169
[76, 360] loss: 1.170
Epoch: 76 -> Loss: 1.09218108654
Epoch: 76 -> Test Accuracy: 50.97
[77, 60] loss: 1.154
[77, 120] loss: 1.180
[77, 180] loss: 1.161
[77, 240] loss: 1.160
[77, 300] loss: 1.176
[77, 360] loss: 1.152
Epoch: 77 -> Loss: 1.00372922421
Epoch: 77 -> Test Accuracy: 51.79
[78, 60] loss: 1.146
[78, 120] loss: 1.149
[78, 180] loss: 1.189
[78, 240] loss: 1.163
[78, 300] loss: 1.161
[78, 360] loss: 1.149
Epoch: 78 -> Loss: 1.34538543224
Epoch: 78 -> Test Accuracy: 51.11
[79, 60] loss: 1.156
[79, 120] loss: 1.174
[79, 180] loss: 1.164
[79, 240] loss: 1.161
[79, 300] loss: 1.169
[79, 360] loss: 1.152
Epoch: 79 -> Loss: 1.16095805168
Epoch: 79 -> Test Accuracy: 51.2
[80, 60] loss: 1.176
[80, 120] loss: 1.150
[80, 180] loss: 1.147
[80, 240] loss: 1.178
[80, 300] loss: 1.164
[80, 360] loss: 1.161
Epoch: 80 -> Loss: 1.29308915138
Epoch: 80 -> Test Accuracy: 51.42
[81, 60] loss: 1.161
[81, 120] loss: 1.159
[81, 180] loss: 1.150
[81, 240] loss: 1.153
[81, 300] loss: 1.166
[81, 360] loss: 1.143
Epoch: 81 -> Loss: 1.29539585114
Epoch: 81 -> Test Accuracy: 51.43
[82, 60] loss: 1.137
[82, 120] loss: 1.164
[82, 180] loss: 1.144
[82, 240] loss: 1.188
[82, 300] loss: 1.161
[82, 360] loss: 1.145
Epoch: 82 -> Loss: 1.00073957443
Epoch: 82 -> Test Accuracy: 51.37
[83, 60] loss: 1.176
[83, 120] loss: 1.162
[83, 180] loss: 1.152
[83, 240] loss: 1.161
[83, 300] loss: 1.167
[83, 360] loss: 1.162
Epoch: 83 -> Loss: 1.1242620945
Epoch: 83 -> Test Accuracy: 52.61
[84, 60] loss: 1.164
[84, 120] loss: 1.163
[84, 180] loss: 1.157
[84, 240] loss: 1.173
[84, 300] loss: 1.143
[84, 360] loss: 1.169
Epoch: 84 -> Loss: 1.27708125114
Epoch: 84 -> Test Accuracy: 50.68
[85, 60] loss: 1.162
[85, 120] loss: 1.176
[85, 180] loss: 1.158
[85, 240] loss: 1.163
[85, 300] loss: 1.162
[85, 360] loss: 1.141
Epoch: 85 -> Loss: 1.06103205681
Epoch: 85 -> Test Accuracy: 51.77
[86, 60] loss: 1.135
[86, 120] loss: 1.119
[86, 180] loss: 1.135
[86, 240] loss: 1.115
[86, 300] loss: 1.122
[86, 360] loss: 1.138
Epoch: 86 -> Loss: 1.16500258446
Epoch: 86 -> Test Accuracy: 52.5
[87, 60] loss: 1.108
[87, 120] loss: 1.115
[87, 180] loss: 1.143
[87, 240] loss: 1.094
[87, 300] loss: 1.123
[87, 360] loss: 1.102
Epoch: 87 -> Loss: 1.16233706474
Epoch: 87 -> Test Accuracy: 52.94
[88, 60] loss: 1.117
[88, 120] loss: 1.119
[88, 180] loss: 1.106
[88, 240] loss: 1.121
[88, 300] loss: 1.105
[88, 360] loss: 1.122
Epoch: 88 -> Loss: 0.972873389721
Epoch: 88 -> Test Accuracy: 52.66
[89, 60] loss: 1.104
[89, 120] loss: 1.094
[89, 180] loss: 1.130
[89, 240] loss: 1.099
[89, 300] loss: 1.113
[89, 360] loss: 1.107
Epoch: 89 -> Loss: 1.1231405735
Epoch: 89 -> Test Accuracy: 53.01
[90, 60] loss: 1.101
[90, 120] loss: 1.120
[90, 180] loss: 1.116
[90, 240] loss: 1.121
[90, 300] loss: 1.114
[90, 360] loss: 1.110
Epoch: 90 -> Loss: 0.941604614258
Epoch: 90 -> Test Accuracy: 53.25
[91, 60] loss: 1.113
[91, 120] loss: 1.102
[91, 180] loss: 1.117
[91, 240] loss: 1.098
[91, 300] loss: 1.109
[91, 360] loss: 1.102
Epoch: 91 -> Loss: 1.06780660152
Epoch: 91 -> Test Accuracy: 52.86
[92, 60] loss: 1.101
[92, 120] loss: 1.123
[92, 180] loss: 1.095
[92, 240] loss: 1.115
[92, 300] loss: 1.097
[92, 360] loss: 1.109
Epoch: 92 -> Loss: 1.26290023327
Epoch: 92 -> Test Accuracy: 53.22
[93, 60] loss: 1.111
[93, 120] loss: 1.121
[93, 180] loss: 1.092
[93, 240] loss: 1.112
[93, 300] loss: 1.102
[93, 360] loss: 1.101
Epoch: 93 -> Loss: 1.0867921114
Epoch: 93 -> Test Accuracy: 53.11
[94, 60] loss: 1.104
[94, 120] loss: 1.115
[94, 180] loss: 1.083
[94, 240] loss: 1.112
[94, 300] loss: 1.108
[94, 360] loss: 1.111
Epoch: 94 -> Loss: 1.05639493465
Epoch: 94 -> Test Accuracy: 53.47
[95, 60] loss: 1.113
[95, 120] loss: 1.090
[95, 180] loss: 1.112
[95, 240] loss: 1.124
[95, 300] loss: 1.106
[95, 360] loss: 1.104
Epoch: 95 -> Loss: 1.07176852226
Epoch: 95 -> Test Accuracy: 52.88
[96, 60] loss: 1.099
[96, 120] loss: 1.113
[96, 180] loss: 1.115
[96, 240] loss: 1.088
[96, 300] loss: 1.105
[96, 360] loss: 1.111
Epoch: 96 -> Loss: 1.01445615292
Epoch: 96 -> Test Accuracy: 53.2
[97, 60] loss: 1.113
[97, 120] loss: 1.092
[97, 180] loss: 1.117
[97, 240] loss: 1.101
[97, 300] loss: 1.110
[97, 360] loss: 1.111
Epoch: 97 -> Loss: 1.16818356514
Epoch: 97 -> Test Accuracy: 53.1
[98, 60] loss: 1.113
[98, 120] loss: 1.097
[98, 180] loss: 1.111
[98, 240] loss: 1.101
[98, 300] loss: 1.075
[98, 360] loss: 1.111
Epoch: 98 -> Loss: 1.11420583725
Epoch: 98 -> Test Accuracy: 53.73
[99, 60] loss: 1.104
[99, 120] loss: 1.107
[99, 180] loss: 1.111
[99, 240] loss: 1.095
[99, 300] loss: 1.083
[99, 360] loss: 1.095
Epoch: 99 -> Loss: 1.22115063667
Epoch: 99 -> Test Accuracy: 53.44
[100, 60] loss: 1.102
[100, 120] loss: 1.093
[100, 180] loss: 1.099
[100, 240] loss: 1.108
[100, 300] loss: 1.098
[100, 360] loss: 1.099
Epoch: 100 -> Loss: 1.22716128826
Epoch: 100 -> Test Accuracy: 53.4
Finished Training
In [10]:
# save variables
fm.save_variable([rot_block4_loss_log, rot_block4_test_accuracy_log, 
                  block4_loss_log, block4_test_accuracy_log, 
                  conv_block4_loss_log, conv_block4_test_accuracy_log], "4_block_net")
In [11]:
# rename files
fm.add_block_to_name(4, [100, 200])

5 Block RotNet

In [6]:
# initialize network
net_block5 = RN.RotNet(num_classes=4, num_conv_block=5, add_avg_pool=False)
In [7]:
# train network
rot_block5_loss_log, _, rot_block5_test_accuracy_log, _, _ = tr.adaptive_learning([0.1, 0.02, 0.004, 0.0008], 
    [60, 120, 160, 200], 0.9, 5e-4, net_block5, criterion, trainloader, None, testloader, rot=['90', '180', '270'])
functionalities/rotation.py:16: UserWarning: torch.range is deprecated in favor of torch.arange and will be removed in 0.5. Note that arange generates values in [start; end), not [start; end].
  flip_idx = torch.range(trans_im.size(2) - 1, 0, -1).long()
functionalities/rotation.py:31: UserWarning: torch.range is deprecated in favor of torch.arange and will be removed in 0.5. Note that arange generates values in [start; end), not [start; end].
  vert_idx = torch.range(image.size(2) - 1, 0, -1).long()
functionalities/rotation.py:35: UserWarning: torch.range is deprecated in favor of torch.arange and will be removed in 0.5. Note that arange generates values in [start; end), not [start; end].
  hor_idx = torch.range(vert_im.size(1) - 1, 0, -1).long()
functionalities/rotation.py:50: UserWarning: torch.range is deprecated in favor of torch.arange and will be removed in 0.5. Note that arange generates values in [start; end), not [start; end].
  vert_idx = torch.range(image.size(2) - 1, 0, -1).long()
[1, 60] loss: 1.242
[1, 120] loss: 1.044
[1, 180] loss: 1.001
[1, 240] loss: 0.947
[1, 300] loss: 0.920
[1, 360] loss: 0.896
Epoch: 1 -> Loss: 0.777884483337
Epoch: 1 -> Test Accuracy: 65.8775
[2, 60] loss: 0.833
[2, 120] loss: 0.809
[2, 180] loss: 0.781
[2, 240] loss: 0.774
[2, 300] loss: 0.744
[2, 360] loss: 0.715
Epoch: 2 -> Loss: 0.648448169231
Epoch: 2 -> Test Accuracy: 73.33
[3, 60] loss: 0.674
[3, 120] loss: 0.659
[3, 180] loss: 0.653
[3, 240] loss: 0.636
[3, 300] loss: 0.637
[3, 360] loss: 0.629
Epoch: 3 -> Loss: 0.59783154726
Epoch: 3 -> Test Accuracy: 76.0625
[4, 60] loss: 0.609
[4, 120] loss: 0.590
[4, 180] loss: 0.575
[4, 240] loss: 0.564
[4, 300] loss: 0.570
[4, 360] loss: 0.568
Epoch: 4 -> Loss: 0.559232831001
Epoch: 4 -> Test Accuracy: 77.84
[5, 60] loss: 0.543
[5, 120] loss: 0.549
[5, 180] loss: 0.526
[5, 240] loss: 0.534
[5, 300] loss: 0.519
[5, 360] loss: 0.541
Epoch: 5 -> Loss: 0.41231456399
Epoch: 5 -> Test Accuracy: 80.7425
[6, 60] loss: 0.506
[6, 120] loss: 0.519
[6, 180] loss: 0.504
[6, 240] loss: 0.478
[6, 300] loss: 0.500
[6, 360] loss: 0.474
Epoch: 6 -> Loss: 0.504078507423
Epoch: 6 -> Test Accuracy: 79.7575
[7, 60] loss: 0.481
[7, 120] loss: 0.468
[7, 180] loss: 0.466
[7, 240] loss: 0.477
[7, 300] loss: 0.475
[7, 360] loss: 0.470
Epoch: 7 -> Loss: 0.382222265005
Epoch: 7 -> Test Accuracy: 80.2325
[8, 60] loss: 0.459
[8, 120] loss: 0.459
[8, 180] loss: 0.446
[8, 240] loss: 0.452
[8, 300] loss: 0.469
[8, 360] loss: 0.443
Epoch: 8 -> Loss: 0.461792856455
Epoch: 8 -> Test Accuracy: 81.92
[9, 60] loss: 0.429
[9, 120] loss: 0.447
[9, 180] loss: 0.456
[9, 240] loss: 0.428
[9, 300] loss: 0.447
[9, 360] loss: 0.441
Epoch: 9 -> Loss: 0.358080148697
Epoch: 9 -> Test Accuracy: 82.645
[10, 60] loss: 0.410
[10, 120] loss: 0.441
[10, 180] loss: 0.415
[10, 240] loss: 0.416
[10, 300] loss: 0.419
[10, 360] loss: 0.440
Epoch: 10 -> Loss: 0.329341083765
Epoch: 10 -> Test Accuracy: 83.3175
[11, 60] loss: 0.420
[11, 120] loss: 0.394
[11, 180] loss: 0.418
[11, 240] loss: 0.409
[11, 300] loss: 0.419
[11, 360] loss: 0.417
Epoch: 11 -> Loss: 0.457412064075
Epoch: 11 -> Test Accuracy: 82.8225
[12, 60] loss: 0.407
[12, 120] loss: 0.395
[12, 180] loss: 0.389
[12, 240] loss: 0.406
[12, 300] loss: 0.412
[12, 360] loss: 0.406
Epoch: 12 -> Loss: 0.360869258642
Epoch: 12 -> Test Accuracy: 83.8975
[13, 60] loss: 0.400
[13, 120] loss: 0.408
[13, 180] loss: 0.399
[13, 240] loss: 0.395
[13, 300] loss: 0.406
[13, 360] loss: 0.377
Epoch: 13 -> Loss: 0.46699398756
Epoch: 13 -> Test Accuracy: 84.5125
[14, 60] loss: 0.389
[14, 120] loss: 0.384
[14, 180] loss: 0.389
[14, 240] loss: 0.369
[14, 300] loss: 0.384
[14, 360] loss: 0.391
Epoch: 14 -> Loss: 0.378018081188
Epoch: 14 -> Test Accuracy: 84.855
[15, 60] loss: 0.387
[15, 120] loss: 0.388
[15, 180] loss: 0.378
[15, 240] loss: 0.380
[15, 300] loss: 0.377
[15, 360] loss: 0.373
Epoch: 15 -> Loss: 0.58175688982
Epoch: 15 -> Test Accuracy: 84.69
[16, 60] loss: 0.376
[16, 120] loss: 0.364
[16, 180] loss: 0.385
[16, 240] loss: 0.381
[16, 300] loss: 0.365
[16, 360] loss: 0.368
Epoch: 16 -> Loss: 0.40565481782
Epoch: 16 -> Test Accuracy: 83.62
[17, 60] loss: 0.350
[17, 120] loss: 0.374
[17, 180] loss: 0.367
[17, 240] loss: 0.372
[17, 300] loss: 0.370
[17, 360] loss: 0.375
Epoch: 17 -> Loss: 0.279655516148
Epoch: 17 -> Test Accuracy: 84.685
[18, 60] loss: 0.360
[18, 120] loss: 0.357
[18, 180] loss: 0.370
[18, 240] loss: 0.361
[18, 300] loss: 0.373
[18, 360] loss: 0.370
Epoch: 18 -> Loss: 0.338039547205
Epoch: 18 -> Test Accuracy: 85.03
[19, 60] loss: 0.349
[19, 120] loss: 0.356
[19, 180] loss: 0.353
[19, 240] loss: 0.354
[19, 300] loss: 0.368
[19, 360] loss: 0.366
Epoch: 19 -> Loss: 0.313662439585
Epoch: 19 -> Test Accuracy: 84.91
[20, 60] loss: 0.358
[20, 120] loss: 0.337
[20, 180] loss: 0.360
[20, 240] loss: 0.359
[20, 300] loss: 0.349
[20, 360] loss: 0.369
Epoch: 20 -> Loss: 0.408251821995
Epoch: 20 -> Test Accuracy: 84.8525
[21, 60] loss: 0.354
[21, 120] loss: 0.350
[21, 180] loss: 0.345
[21, 240] loss: 0.348
[21, 300] loss: 0.350
[21, 360] loss: 0.371
Epoch: 21 -> Loss: 0.233082175255
Epoch: 21 -> Test Accuracy: 85.165
[22, 60] loss: 0.333
[22, 120] loss: 0.354
[22, 180] loss: 0.347
[22, 240] loss: 0.363
[22, 300] loss: 0.339
[22, 360] loss: 0.367
Epoch: 22 -> Loss: 0.300312995911
Epoch: 22 -> Test Accuracy: 85.2375
[23, 60] loss: 0.340
[23, 120] loss: 0.348
[23, 180] loss: 0.341
[23, 240] loss: 0.342
[23, 300] loss: 0.349
[23, 360] loss: 0.343
Epoch: 23 -> Loss: 0.360540628433
Epoch: 23 -> Test Accuracy: 86.1225
[24, 60] loss: 0.337
[24, 120] loss: 0.346
[24, 180] loss: 0.337
[24, 240] loss: 0.350
[24, 300] loss: 0.339
[24, 360] loss: 0.349
Epoch: 24 -> Loss: 0.346710771322
Epoch: 24 -> Test Accuracy: 85.545
[25, 60] loss: 0.336
[25, 120] loss: 0.350
[25, 180] loss: 0.334
[25, 240] loss: 0.347
[25, 300] loss: 0.344
[25, 360] loss: 0.341
Epoch: 25 -> Loss: 0.445628076792
Epoch: 25 -> Test Accuracy: 85.6525
[26, 60] loss: 0.336
[26, 120] loss: 0.339
[26, 180] loss: 0.345
[26, 240] loss: 0.354
[26, 300] loss: 0.346
[26, 360] loss: 0.333
Epoch: 26 -> Loss: 0.357057720423
Epoch: 26 -> Test Accuracy: 85.71
[27, 60] loss: 0.332
[27, 120] loss: 0.351
[27, 180] loss: 0.335
[27, 240] loss: 0.339
[27, 300] loss: 0.328
[27, 360] loss: 0.330
Epoch: 27 -> Loss: 0.435141414404
Epoch: 27 -> Test Accuracy: 85.015
[28, 60] loss: 0.332
[28, 120] loss: 0.323
[28, 180] loss: 0.329
[28, 240] loss: 0.350
[28, 300] loss: 0.334
[28, 360] loss: 0.343
Epoch: 28 -> Loss: 0.30782777071
Epoch: 28 -> Test Accuracy: 85.1275
[29, 60] loss: 0.343
[29, 120] loss: 0.332
[29, 180] loss: 0.335
[29, 240] loss: 0.324
[29, 300] loss: 0.327
[29, 360] loss: 0.345
Epoch: 29 -> Loss: 0.337554395199
Epoch: 29 -> Test Accuracy: 86.305
[30, 60] loss: 0.309
[30, 120] loss: 0.323
[30, 180] loss: 0.355
[30, 240] loss: 0.330
[30, 300] loss: 0.338
[30, 360] loss: 0.334
Epoch: 30 -> Loss: 0.214543700218
Epoch: 30 -> Test Accuracy: 85.5475
[31, 60] loss: 0.330
[31, 120] loss: 0.334
[31, 180] loss: 0.318
[31, 240] loss: 0.338
[31, 300] loss: 0.331
[31, 360] loss: 0.341
Epoch: 31 -> Loss: 0.232660770416
Epoch: 31 -> Test Accuracy: 86.6125
[32, 60] loss: 0.319
[32, 120] loss: 0.329
[32, 180] loss: 0.330
[32, 240] loss: 0.336
[32, 300] loss: 0.328
[32, 360] loss: 0.335
Epoch: 32 -> Loss: 0.370219677687
Epoch: 32 -> Test Accuracy: 85.26
[33, 60] loss: 0.319
[33, 120] loss: 0.331
[33, 180] loss: 0.323
[33, 240] loss: 0.320
[33, 300] loss: 0.342
[33, 360] loss: 0.320
Epoch: 33 -> Loss: 0.436576515436
Epoch: 33 -> Test Accuracy: 86.49
[34, 60] loss: 0.305
[34, 120] loss: 0.324
[34, 180] loss: 0.335
[34, 240] loss: 0.329
[34, 300] loss: 0.325
[34, 360] loss: 0.323
Epoch: 34 -> Loss: 0.451147943735
Epoch: 34 -> Test Accuracy: 85.5075
[35, 60] loss: 0.318
[35, 120] loss: 0.307
[35, 180] loss: 0.336
[35, 240] loss: 0.323
[35, 300] loss: 0.327
[35, 360] loss: 0.322
Epoch: 35 -> Loss: 0.368377655745
Epoch: 35 -> Test Accuracy: 86.3525
[36, 60] loss: 0.317
[36, 120] loss: 0.308
[36, 180] loss: 0.318
[36, 240] loss: 0.328
[36, 300] loss: 0.335
[36, 360] loss: 0.316
Epoch: 36 -> Loss: 0.330079138279
Epoch: 36 -> Test Accuracy: 86.05
[37, 60] loss: 0.319
[37, 120] loss: 0.316
[37, 180] loss: 0.327
[37, 240] loss: 0.321
[37, 300] loss: 0.324
[37, 360] loss: 0.323
Epoch: 37 -> Loss: 0.301078379154
Epoch: 37 -> Test Accuracy: 86.45
[38, 60] loss: 0.302
[38, 120] loss: 0.310
[38, 180] loss: 0.333
[38, 240] loss: 0.319
[38, 300] loss: 0.326
[38, 360] loss: 0.340
Epoch: 38 -> Loss: 0.334967881441
Epoch: 38 -> Test Accuracy: 86.1275
[39, 60] loss: 0.312
[39, 120] loss: 0.329
[39, 180] loss: 0.315
[39, 240] loss: 0.326
[39, 300] loss: 0.309
[39, 360] loss: 0.324
Epoch: 39 -> Loss: 0.341781675816
Epoch: 39 -> Test Accuracy: 85.74
[40, 60] loss: 0.318
[40, 120] loss: 0.307
[40, 180] loss: 0.328
[40, 240] loss: 0.310
[40, 300] loss: 0.331
[40, 360] loss: 0.332
Epoch: 40 -> Loss: 0.517393827438
Epoch: 40 -> Test Accuracy: 85.225
[41, 60] loss: 0.308
[41, 120] loss: 0.326
[41, 180] loss: 0.333
[41, 240] loss: 0.306
[41, 300] loss: 0.324
[41, 360] loss: 0.315
Epoch: 41 -> Loss: 0.29975682497
Epoch: 41 -> Test Accuracy: 85.2725
[42, 60] loss: 0.309
[42, 120] loss: 0.311
[42, 180] loss: 0.318
[42, 240] loss: 0.312
[42, 300] loss: 0.321
[42, 360] loss: 0.331
Epoch: 42 -> Loss: 0.295903921127
Epoch: 42 -> Test Accuracy: 86.9525
[43, 60] loss: 0.304
[43, 120] loss: 0.327
[43, 180] loss: 0.303
[43, 240] loss: 0.327
[43, 300] loss: 0.322
[43, 360] loss: 0.330
Epoch: 43 -> Loss: 0.400561511517
Epoch: 43 -> Test Accuracy: 86.195
[44, 60] loss: 0.297
[44, 120] loss: 0.318
[44, 180] loss: 0.315
[44, 240] loss: 0.322
[44, 300] loss: 0.316
[44, 360] loss: 0.325
Epoch: 44 -> Loss: 0.30678999424
Epoch: 44 -> Test Accuracy: 85.77
[45, 60] loss: 0.303
[45, 120] loss: 0.317
[45, 180] loss: 0.321
[45, 240] loss: 0.314
[45, 300] loss: 0.306
[45, 360] loss: 0.328
Epoch: 45 -> Loss: 0.355306535959
Epoch: 45 -> Test Accuracy: 86.19
[46, 60] loss: 0.302
[46, 120] loss: 0.307
[46, 180] loss: 0.314
[46, 240] loss: 0.311
[46, 300] loss: 0.320
[46, 360] loss: 0.312
Epoch: 46 -> Loss: 0.31866440177
Epoch: 46 -> Test Accuracy: 85.425
[47, 60] loss: 0.312
[47, 120] loss: 0.320
[47, 180] loss: 0.314
[47, 240] loss: 0.302
[47, 300] loss: 0.320
[47, 360] loss: 0.314
Epoch: 47 -> Loss: 0.410285294056
Epoch: 47 -> Test Accuracy: 84.9575
[48, 60] loss: 0.304
[48, 120] loss: 0.310
[48, 180] loss: 0.321
[48, 240] loss: 0.313
[48, 300] loss: 0.316
[48, 360] loss: 0.317
Epoch: 48 -> Loss: 0.235875204206
Epoch: 48 -> Test Accuracy: 86.585
[49, 60] loss: 0.306
[49, 120] loss: 0.306
[49, 180] loss: 0.308
[49, 240] loss: 0.315
[49, 300] loss: 0.315
[49, 360] loss: 0.319
Epoch: 49 -> Loss: 0.37031275034
Epoch: 49 -> Test Accuracy: 86.4925
[50, 60] loss: 0.308
[50, 120] loss: 0.301
[50, 180] loss: 0.312
[50, 240] loss: 0.303
[50, 300] loss: 0.324
[50, 360] loss: 0.314
Epoch: 50 -> Loss: 0.308477640152
Epoch: 50 -> Test Accuracy: 85.06
[51, 60] loss: 0.299
[51, 120] loss: 0.304
[51, 180] loss: 0.319
[51, 240] loss: 0.318
[51, 300] loss: 0.311
[51, 360] loss: 0.305
Epoch: 51 -> Loss: 0.378709405661
Epoch: 51 -> Test Accuracy: 86.1075
[52, 60] loss: 0.306
[52, 120] loss: 0.311
[52, 180] loss: 0.311
[52, 240] loss: 0.314
[52, 300] loss: 0.306
[52, 360] loss: 0.325
Epoch: 52 -> Loss: 0.296770453453
Epoch: 52 -> Test Accuracy: 86.52
[53, 60] loss: 0.301
[53, 120] loss: 0.305
[53, 180] loss: 0.312
[53, 240] loss: 0.300
[53, 300] loss: 0.309
[53, 360] loss: 0.306
Epoch: 53 -> Loss: 0.296748191118
Epoch: 53 -> Test Accuracy: 86.5125
[54, 60] loss: 0.295
[54, 120] loss: 0.303
[54, 180] loss: 0.310
[54, 240] loss: 0.313
[54, 300] loss: 0.312
[54, 360] loss: 0.302
Epoch: 54 -> Loss: 0.283517181873
Epoch: 54 -> Test Accuracy: 86.0925
[55, 60] loss: 0.294
[55, 120] loss: 0.298
[55, 180] loss: 0.313
[55, 240] loss: 0.306
[55, 300] loss: 0.321
[55, 360] loss: 0.313
Epoch: 55 -> Loss: 0.341846287251
Epoch: 55 -> Test Accuracy: 86.625
[56, 60] loss: 0.283
[56, 120] loss: 0.304
[56, 180] loss: 0.305
[56, 240] loss: 0.316
[56, 300] loss: 0.315
[56, 360] loss: 0.319
Epoch: 56 -> Loss: 0.268593251705
Epoch: 56 -> Test Accuracy: 85.5575
[57, 60] loss: 0.296
[57, 120] loss: 0.292
[57, 180] loss: 0.307
[57, 240] loss: 0.309
[57, 300] loss: 0.323
[57, 360] loss: 0.306
Epoch: 57 -> Loss: 0.299621284008
Epoch: 57 -> Test Accuracy: 85.515
[58, 60] loss: 0.299
[58, 120] loss: 0.303
[58, 180] loss: 0.304
[58, 240] loss: 0.307
[58, 300] loss: 0.310
[58, 360] loss: 0.303
Epoch: 58 -> Loss: 0.391120016575
Epoch: 58 -> Test Accuracy: 87.0725
[59, 60] loss: 0.292
[59, 120] loss: 0.300
[59, 180] loss: 0.294
[59, 240] loss: 0.317
[59, 300] loss: 0.311
[59, 360] loss: 0.316
Epoch: 59 -> Loss: 0.31570148468
Epoch: 59 -> Test Accuracy: 87.095
[60, 60] loss: 0.307
[60, 120] loss: 0.296
[60, 180] loss: 0.302
[60, 240] loss: 0.302
[60, 300] loss: 0.296
[60, 360] loss: 0.324
Epoch: 60 -> Loss: 0.374734848738
Epoch: 60 -> Test Accuracy: 86.595
[61, 60] loss: 0.232
[61, 120] loss: 0.200
[61, 180] loss: 0.185
[61, 240] loss: 0.192
[61, 300] loss: 0.186
[61, 360] loss: 0.183
Epoch: 61 -> Loss: 0.157360211015
Epoch: 61 -> Test Accuracy: 90.7825
[62, 60] loss: 0.166
[62, 120] loss: 0.165
[62, 180] loss: 0.174
[62, 240] loss: 0.163
[62, 300] loss: 0.169
[62, 360] loss: 0.166
Epoch: 62 -> Loss: 0.198719024658
Epoch: 62 -> Test Accuracy: 91.1625
[63, 60] loss: 0.150
[63, 120] loss: 0.150
[63, 180] loss: 0.165
[63, 240] loss: 0.157
[63, 300] loss: 0.167
[63, 360] loss: 0.160
Epoch: 63 -> Loss: 0.135304674506
Epoch: 63 -> Test Accuracy: 90.925
[64, 60] loss: 0.139
[64, 120] loss: 0.154
[64, 180] loss: 0.154
[64, 240] loss: 0.157
[64, 300] loss: 0.150
[64, 360] loss: 0.159
Epoch: 64 -> Loss: 0.101378165185
Epoch: 64 -> Test Accuracy: 90.9575
[65, 60] loss: 0.146
[65, 120] loss: 0.155
[65, 180] loss: 0.152
[65, 240] loss: 0.152
[65, 300] loss: 0.146
[65, 360] loss: 0.151
Epoch: 65 -> Loss: 0.172543406487
Epoch: 65 -> Test Accuracy: 90.99
[66, 60] loss: 0.144
[66, 120] loss: 0.148
[66, 180] loss: 0.162
[66, 240] loss: 0.144
[66, 300] loss: 0.153
[66, 360] loss: 0.147
Epoch: 66 -> Loss: 0.122496888041
Epoch: 66 -> Test Accuracy: 91.1525
[67, 60] loss: 0.137
[67, 120] loss: 0.139
[67, 180] loss: 0.144
[67, 240] loss: 0.160
[67, 300] loss: 0.151
[67, 360] loss: 0.158
Epoch: 67 -> Loss: 0.145913600922
Epoch: 67 -> Test Accuracy: 91.03
[68, 60] loss: 0.137
[68, 120] loss: 0.142
[68, 180] loss: 0.148
[68, 240] loss: 0.160
[68, 300] loss: 0.144
[68, 360] loss: 0.151
Epoch: 68 -> Loss: 0.212457686663
Epoch: 68 -> Test Accuracy: 90.7575
[69, 60] loss: 0.142
[69, 120] loss: 0.144
[69, 180] loss: 0.144
[69, 240] loss: 0.152
[69, 300] loss: 0.149
[69, 360] loss: 0.151
Epoch: 69 -> Loss: 0.220518514514
Epoch: 69 -> Test Accuracy: 90.4725
[70, 60] loss: 0.145
[70, 120] loss: 0.156
[70, 180] loss: 0.145
[70, 240] loss: 0.144
[70, 300] loss: 0.145
[70, 360] loss: 0.151
Epoch: 70 -> Loss: 0.16164290905
Epoch: 70 -> Test Accuracy: 90.055
[71, 60] loss: 0.142
[71, 120] loss: 0.140
[71, 180] loss: 0.157
[71, 240] loss: 0.146
[71, 300] loss: 0.151
[71, 360] loss: 0.149
Epoch: 71 -> Loss: 0.0878470093012
Epoch: 71 -> Test Accuracy: 90.5975
[72, 60] loss: 0.136
[72, 120] loss: 0.141
[72, 180] loss: 0.149
[72, 240] loss: 0.148
[72, 300] loss: 0.150
[72, 360] loss: 0.158
Epoch: 72 -> Loss: 0.19203093648
Epoch: 72 -> Test Accuracy: 90.605
[73, 60] loss: 0.133
[73, 120] loss: 0.141
[73, 180] loss: 0.149
[73, 240] loss: 0.151
[73, 300] loss: 0.154
[73, 360] loss: 0.152
Epoch: 73 -> Loss: 0.157403796911
Epoch: 73 -> Test Accuracy: 90.5425
[74, 60] loss: 0.147
[74, 120] loss: 0.143
[74, 180] loss: 0.151
[74, 240] loss: 0.149
[74, 300] loss: 0.161
[74, 360] loss: 0.148
Epoch: 74 -> Loss: 0.0985128059983
Epoch: 74 -> Test Accuracy: 90.23
[75, 60] loss: 0.137
[75, 120] loss: 0.143
[75, 180] loss: 0.146
[75, 240] loss: 0.148
[75, 300] loss: 0.161
[75, 360] loss: 0.153
Epoch: 75 -> Loss: 0.192423030734
Epoch: 75 -> Test Accuracy: 90.2825
[76, 60] loss: 0.140
[76, 120] loss: 0.147
[76, 180] loss: 0.141
[76, 240] loss: 0.154
[76, 300] loss: 0.152
[76, 360] loss: 0.154
Epoch: 76 -> Loss: 0.21979098022
Epoch: 76 -> Test Accuracy: 90.7475
[77, 60] loss: 0.142
[77, 120] loss: 0.136
[77, 180] loss: 0.159
[77, 240] loss: 0.158
[77, 300] loss: 0.155
[77, 360] loss: 0.148
Epoch: 77 -> Loss: 0.198052495718
Epoch: 77 -> Test Accuracy: 89.8725
[78, 60] loss: 0.143
[78, 120] loss: 0.141
[78, 180] loss: 0.160
[78, 240] loss: 0.143
[78, 300] loss: 0.155
[78, 360] loss: 0.156
Epoch: 78 -> Loss: 0.118901535869
Epoch: 78 -> Test Accuracy: 90.0175
[79, 60] loss: 0.142
[79, 120] loss: 0.145
[79, 180] loss: 0.151
[79, 240] loss: 0.150
[79, 300] loss: 0.145
[79, 360] loss: 0.155
Epoch: 79 -> Loss: 0.174518898129
Epoch: 79 -> Test Accuracy: 90.3925
[80, 60] loss: 0.131
[80, 120] loss: 0.154
[80, 180] loss: 0.153
[80, 240] loss: 0.143
[80, 300] loss: 0.156
[80, 360] loss: 0.151
Epoch: 80 -> Loss: 0.176137581468
Epoch: 80 -> Test Accuracy: 90.425
[81, 60] loss: 0.139
[81, 120] loss: 0.153
[81, 180] loss: 0.142
[81, 240] loss: 0.143
[81, 300] loss: 0.150
[81, 360] loss: 0.158
Epoch: 81 -> Loss: 0.115477837622
Epoch: 81 -> Test Accuracy: 90.4975
[82, 60] loss: 0.137
[82, 120] loss: 0.148
[82, 180] loss: 0.149
[82, 240] loss: 0.155
[82, 300] loss: 0.151
[82, 360] loss: 0.160
Epoch: 82 -> Loss: 0.0695660114288
Epoch: 82 -> Test Accuracy: 90.59
[83, 60] loss: 0.133
[83, 120] loss: 0.147
[83, 180] loss: 0.137
[83, 240] loss: 0.152
[83, 300] loss: 0.156
[83, 360] loss: 0.160
Epoch: 83 -> Loss: 0.213991358876
Epoch: 83 -> Test Accuracy: 90.3625
[84, 60] loss: 0.133
[84, 120] loss: 0.142
[84, 180] loss: 0.141
[84, 240] loss: 0.151
[84, 300] loss: 0.157
[84, 360] loss: 0.156
Epoch: 84 -> Loss: 0.158650770783
Epoch: 84 -> Test Accuracy: 89.905
[85, 60] loss: 0.144
[85, 120] loss: 0.142
[85, 180] loss: 0.154
[85, 240] loss: 0.142
[85, 300] loss: 0.153
[85, 360] loss: 0.155
Epoch: 85 -> Loss: 0.133175000548
Epoch: 85 -> Test Accuracy: 89.7075
[86, 60] loss: 0.137
[86, 120] loss: 0.145
[86, 180] loss: 0.148
[86, 240] loss: 0.143
[86, 300] loss: 0.152
[86, 360] loss: 0.154
Epoch: 86 -> Loss: 0.0884124040604
Epoch: 86 -> Test Accuracy: 90.0125
[87, 60] loss: 0.142
[87, 120] loss: 0.138
[87, 180] loss: 0.149
[87, 240] loss: 0.154
[87, 300] loss: 0.144
[87, 360] loss: 0.151
Epoch: 87 -> Loss: 0.109030939639
Epoch: 87 -> Test Accuracy: 89.9325
[88, 60] loss: 0.138
[88, 120] loss: 0.146
[88, 180] loss: 0.143
[88, 240] loss: 0.150
[88, 300] loss: 0.146
[88, 360] loss: 0.149
Epoch: 88 -> Loss: 0.149111643434
Epoch: 88 -> Test Accuracy: 90.465
[89, 60] loss: 0.135
[89, 120] loss: 0.141
[89, 180] loss: 0.142
[89, 240] loss: 0.149
[89, 300] loss: 0.145
[89, 360] loss: 0.151
Epoch: 89 -> Loss: 0.146894484758
Epoch: 89 -> Test Accuracy: 89.8125
[90, 60] loss: 0.139
[90, 120] loss: 0.140
[90, 180] loss: 0.145
[90, 240] loss: 0.148
[90, 300] loss: 0.152
[90, 360] loss: 0.137
Epoch: 90 -> Loss: 0.133741512895
Epoch: 90 -> Test Accuracy: 89.9725
[91, 60] loss: 0.141
[91, 120] loss: 0.137
[91, 180] loss: 0.144
[91, 240] loss: 0.138
[91, 300] loss: 0.153
[91, 360] loss: 0.149
Epoch: 91 -> Loss: 0.134075343609
Epoch: 91 -> Test Accuracy: 90.3725
[92, 60] loss: 0.129
[92, 120] loss: 0.139
[92, 180] loss: 0.141
[92, 240] loss: 0.136
[92, 300] loss: 0.148
[92, 360] loss: 0.147
Epoch: 92 -> Loss: 0.345908850431
Epoch: 92 -> Test Accuracy: 90.355
[93, 60] loss: 0.125
[93, 120] loss: 0.130
[93, 180] loss: 0.154
[93, 240] loss: 0.137
[93, 300] loss: 0.155
[93, 360] loss: 0.148
Epoch: 93 -> Loss: 0.207707047462
Epoch: 93 -> Test Accuracy: 90.1275
[94, 60] loss: 0.141
[94, 120] loss: 0.142
[94, 180] loss: 0.140
[94, 240] loss: 0.134
[94, 300] loss: 0.147
[94, 360] loss: 0.152
Epoch: 94 -> Loss: 0.155148491263
Epoch: 94 -> Test Accuracy: 90.35
[95, 60] loss: 0.133
[95, 120] loss: 0.136
[95, 180] loss: 0.142
[95, 240] loss: 0.152
[95, 300] loss: 0.148
[95, 360] loss: 0.147
Epoch: 95 -> Loss: 0.128780096769
Epoch: 95 -> Test Accuracy: 90.355
[96, 60] loss: 0.127
[96, 120] loss: 0.137
[96, 180] loss: 0.142
[96, 240] loss: 0.136
[96, 300] loss: 0.152
[96, 360] loss: 0.144
Epoch: 96 -> Loss: 0.197761058807
Epoch: 96 -> Test Accuracy: 90.1825
[97, 60] loss: 0.134
[97, 120] loss: 0.130
[97, 180] loss: 0.139
[97, 240] loss: 0.137
[97, 300] loss: 0.151
[97, 360] loss: 0.146
Epoch: 97 -> Loss: 0.144468128681
Epoch: 97 -> Test Accuracy: 89.9425
[98, 60] loss: 0.127
[98, 120] loss: 0.130
[98, 180] loss: 0.137
[98, 240] loss: 0.145
[98, 300] loss: 0.146
[98, 360] loss: 0.142
Epoch: 98 -> Loss: 0.123912729323
Epoch: 98 -> Test Accuracy: 90.1125
[99, 60] loss: 0.136
[99, 120] loss: 0.136
[99, 180] loss: 0.143
[99, 240] loss: 0.136
[99, 300] loss: 0.146
[99, 360] loss: 0.144
Epoch: 99 -> Loss: 0.244579985738
Epoch: 99 -> Test Accuracy: 90.3475
[100, 60] loss: 0.131
[100, 120] loss: 0.144
[100, 180] loss: 0.138
[100, 240] loss: 0.143
[100, 300] loss: 0.143
[100, 360] loss: 0.147
Epoch: 100 -> Loss: 0.0971162691712
Epoch: 100 -> Test Accuracy: 90.275
[101, 60] loss: 0.126
[101, 120] loss: 0.135
[101, 180] loss: 0.141
[101, 240] loss: 0.142
[101, 300] loss: 0.141
[101, 360] loss: 0.152
Epoch: 101 -> Loss: 0.122770212591
Epoch: 101 -> Test Accuracy: 90.2575
[102, 60] loss: 0.132
[102, 120] loss: 0.133
[102, 180] loss: 0.135
[102, 240] loss: 0.143
[102, 300] loss: 0.138
[102, 360] loss: 0.141
Epoch: 102 -> Loss: 0.149077519774
Epoch: 102 -> Test Accuracy: 90.205
[103, 60] loss: 0.134
[103, 120] loss: 0.136
[103, 180] loss: 0.133
[103, 240] loss: 0.139
[103, 300] loss: 0.144
[103, 360] loss: 0.151
Epoch: 103 -> Loss: 0.156240969896
Epoch: 103 -> Test Accuracy: 90.26
[104, 60] loss: 0.131
[104, 120] loss: 0.137
[104, 180] loss: 0.142
[104, 240] loss: 0.138
[104, 300] loss: 0.147
[104, 360] loss: 0.146
Epoch: 104 -> Loss: 0.235925555229
Epoch: 104 -> Test Accuracy: 90.0275
[105, 60] loss: 0.124
[105, 120] loss: 0.141
[105, 180] loss: 0.131
[105, 240] loss: 0.143
[105, 300] loss: 0.140
[105, 360] loss: 0.144
Epoch: 105 -> Loss: 0.107051491737
Epoch: 105 -> Test Accuracy: 90.52
[106, 60] loss: 0.128
[106, 120] loss: 0.129
[106, 180] loss: 0.137
[106, 240] loss: 0.150
[106, 300] loss: 0.133
[106, 360] loss: 0.137
Epoch: 106 -> Loss: 0.121595367789
Epoch: 106 -> Test Accuracy: 90.45
[107, 60] loss: 0.128
[107, 120] loss: 0.135
[107, 180] loss: 0.132
[107, 240] loss: 0.135
[107, 300] loss: 0.132
[107, 360] loss: 0.147
Epoch: 107 -> Loss: 0.266189336777
Epoch: 107 -> Test Accuracy: 90.4675
[108, 60] loss: 0.133
[108, 120] loss: 0.125
[108, 180] loss: 0.138
[108, 240] loss: 0.141
[108, 300] loss: 0.146
[108, 360] loss: 0.141
Epoch: 108 -> Loss: 0.0670185759664
Epoch: 108 -> Test Accuracy: 90.3825
[109, 60] loss: 0.122
[109, 120] loss: 0.130
[109, 180] loss: 0.135
[109, 240] loss: 0.138
[109, 300] loss: 0.133
[109, 360] loss: 0.151
Epoch: 109 -> Loss: 0.18507103622
Epoch: 109 -> Test Accuracy: 89.91
[110, 60] loss: 0.117
[110, 120] loss: 0.137
[110, 180] loss: 0.137
[110, 240] loss: 0.142
[110, 300] loss: 0.139
[110, 360] loss: 0.133
Epoch: 110 -> Loss: 0.130577296019
Epoch: 110 -> Test Accuracy: 90.96
[111, 60] loss: 0.127
[111, 120] loss: 0.134
[111, 180] loss: 0.133
[111, 240] loss: 0.131
[111, 300] loss: 0.133
[111, 360] loss: 0.147
Epoch: 111 -> Loss: 0.106502607465
Epoch: 111 -> Test Accuracy: 90.0725
[112, 60] loss: 0.120
[112, 120] loss: 0.131
[112, 180] loss: 0.142
[112, 240] loss: 0.130
[112, 300] loss: 0.144
[112, 360] loss: 0.136
Epoch: 112 -> Loss: 0.0815871208906
Epoch: 112 -> Test Accuracy: 90.205
[113, 60] loss: 0.126
[113, 120] loss: 0.138
[113, 180] loss: 0.131
[113, 240] loss: 0.131
[113, 300] loss: 0.142
[113, 360] loss: 0.142
Epoch: 113 -> Loss: 0.189298853278
Epoch: 113 -> Test Accuracy: 90.42
[114, 60] loss: 0.124
[114, 120] loss: 0.125
[114, 180] loss: 0.133
[114, 240] loss: 0.139
[114, 300] loss: 0.143
[114, 360] loss: 0.140
Epoch: 114 -> Loss: 0.246730536222
Epoch: 114 -> Test Accuracy: 90.03
[115, 60] loss: 0.124
[115, 120] loss: 0.122
[115, 180] loss: 0.131
[115, 240] loss: 0.145
[115, 300] loss: 0.134
[115, 360] loss: 0.147
Epoch: 115 -> Loss: 0.148653298616
Epoch: 115 -> Test Accuracy: 90.3875
[116, 60] loss: 0.123
[116, 120] loss: 0.126
[116, 180] loss: 0.135
[116, 240] loss: 0.129
[116, 300] loss: 0.142
[116, 360] loss: 0.143
Epoch: 116 -> Loss: 0.190787643194
Epoch: 116 -> Test Accuracy: 89.7875
[117, 60] loss: 0.121
[117, 120] loss: 0.130
[117, 180] loss: 0.136
[117, 240] loss: 0.142
[117, 300] loss: 0.132
[117, 360] loss: 0.136
Epoch: 117 -> Loss: 0.130016759038
Epoch: 117 -> Test Accuracy: 90.53
[118, 60] loss: 0.124
[118, 120] loss: 0.126
[118, 180] loss: 0.139
[118, 240] loss: 0.135
[118, 300] loss: 0.138
[118, 360] loss: 0.133
Epoch: 118 -> Loss: 0.168916806579
Epoch: 118 -> Test Accuracy: 90.1275
[119, 60] loss: 0.123
[119, 120] loss: 0.133
[119, 180] loss: 0.132
[119, 240] loss: 0.135
[119, 300] loss: 0.131
[119, 360] loss: 0.128
Epoch: 119 -> Loss: 0.16822052002
Epoch: 119 -> Test Accuracy: 90.69
[120, 60] loss: 0.125
[120, 120] loss: 0.129
[120, 180] loss: 0.121
[120, 240] loss: 0.139
[120, 300] loss: 0.136
[120, 360] loss: 0.137
Epoch: 120 -> Loss: 0.0735254511237
Epoch: 120 -> Test Accuracy: 90.34
[121, 60] loss: 0.102
[121, 120] loss: 0.072
[121, 180] loss: 0.068
[121, 240] loss: 0.065
[121, 300] loss: 0.065
[121, 360] loss: 0.062
Epoch: 121 -> Loss: 0.0345267057419
Epoch: 121 -> Test Accuracy: 92.1575
[122, 60] loss: 0.056
[122, 120] loss: 0.051
[122, 180] loss: 0.053
[122, 240] loss: 0.055
[122, 300] loss: 0.055
[122, 360] loss: 0.058
Epoch: 122 -> Loss: 0.0269163753837
Epoch: 122 -> Test Accuracy: 92.3825
[123, 60] loss: 0.048
[123, 120] loss: 0.050
[123, 180] loss: 0.049
[123, 240] loss: 0.049
[123, 300] loss: 0.050
[123, 360] loss: 0.049
Epoch: 123 -> Loss: 0.0529579408467
Epoch: 123 -> Test Accuracy: 92.18
[124, 60] loss: 0.041
[124, 120] loss: 0.038
[124, 180] loss: 0.043
[124, 240] loss: 0.047
[124, 300] loss: 0.043
[124, 360] loss: 0.043
Epoch: 124 -> Loss: 0.0119058378041
Epoch: 124 -> Test Accuracy: 92.0225
[125, 60] loss: 0.043
[125, 120] loss: 0.038
[125, 180] loss: 0.041
[125, 240] loss: 0.043
[125, 300] loss: 0.039
[125, 360] loss: 0.038
Epoch: 125 -> Loss: 0.084841221571
Epoch: 125 -> Test Accuracy: 92.2125
[126, 60] loss: 0.034
[126, 120] loss: 0.033
[126, 180] loss: 0.038
[126, 240] loss: 0.039
[126, 300] loss: 0.041
[126, 360] loss: 0.040
Epoch: 126 -> Loss: 0.0972911864519
Epoch: 126 -> Test Accuracy: 92.1425
[127, 60] loss: 0.036
[127, 120] loss: 0.034
[127, 180] loss: 0.034
[127, 240] loss: 0.035
[127, 300] loss: 0.037
[127, 360] loss: 0.035
Epoch: 127 -> Loss: 0.00685061747208
Epoch: 127 -> Test Accuracy: 92.0225
[128, 60] loss: 0.033
[128, 120] loss: 0.035
[128, 180] loss: 0.033
[128, 240] loss: 0.034
[128, 300] loss: 0.037
[128, 360] loss: 0.036
Epoch: 128 -> Loss: 0.0581314563751
Epoch: 128 -> Test Accuracy: 91.9325
[129, 60] loss: 0.033
[129, 120] loss: 0.032
[129, 180] loss: 0.034
[129, 240] loss: 0.034
[129, 300] loss: 0.033
[129, 360] loss: 0.033
Epoch: 129 -> Loss: 0.0307410992682
Epoch: 129 -> Test Accuracy: 92.1625
[130, 60] loss: 0.032
[130, 120] loss: 0.030
[130, 180] loss: 0.031
[130, 240] loss: 0.034
[130, 300] loss: 0.032
[130, 360] loss: 0.031
Epoch: 130 -> Loss: 0.0263896342367
Epoch: 130 -> Test Accuracy: 92.0975
[131, 60] loss: 0.032
[131, 120] loss: 0.032
[131, 180] loss: 0.029
[131, 240] loss: 0.032
[131, 300] loss: 0.035
[131, 360] loss: 0.032
Epoch: 131 -> Loss: 0.0555750355124
Epoch: 131 -> Test Accuracy: 92.0025
[132, 60] loss: 0.032
[132, 120] loss: 0.030
[132, 180] loss: 0.029
[132, 240] loss: 0.031
[132, 300] loss: 0.030
[132, 360] loss: 0.033
Epoch: 132 -> Loss: 0.0544689483941
Epoch: 132 -> Test Accuracy: 92.13
[133, 60] loss: 0.029
[133, 120] loss: 0.029
[133, 180] loss: 0.028
[133, 240] loss: 0.028
[133, 300] loss: 0.030
[133, 360] loss: 0.033
Epoch: 133 -> Loss: 0.0429929457605
Epoch: 133 -> Test Accuracy: 91.7125
[134, 60] loss: 0.031
[134, 120] loss: 0.031
[134, 180] loss: 0.028
[134, 240] loss: 0.028
[134, 300] loss: 0.026
[134, 360] loss: 0.032
Epoch: 134 -> Loss: 0.0229850467294
Epoch: 134 -> Test Accuracy: 92.115
[135, 60] loss: 0.029
[135, 120] loss: 0.028
[135, 180] loss: 0.030
[135, 240] loss: 0.027
[135, 300] loss: 0.027
[135, 360] loss: 0.031
Epoch: 135 -> Loss: 0.0190198067576
Epoch: 135 -> Test Accuracy: 91.7575
[136, 60] loss: 0.029
[136, 120] loss: 0.028
[136, 180] loss: 0.028
[136, 240] loss: 0.028
[136, 300] loss: 0.028
[136, 360] loss: 0.027
Epoch: 136 -> Loss: 0.0446188338101
Epoch: 136 -> Test Accuracy: 91.91
[137, 60] loss: 0.027
[137, 120] loss: 0.027
[137, 180] loss: 0.026
[137, 240] loss: 0.027
[137, 300] loss: 0.031
[137, 360] loss: 0.028
Epoch: 137 -> Loss: 0.0321652516723
Epoch: 137 -> Test Accuracy: 92.0175
[138, 60] loss: 0.025
[138, 120] loss: 0.027
[138, 180] loss: 0.028
[138, 240] loss: 0.027
[138, 300] loss: 0.028
[138, 360] loss: 0.029
Epoch: 138 -> Loss: 0.0293081011623
Epoch: 138 -> Test Accuracy: 91.9175
[139, 60] loss: 0.025
[139, 120] loss: 0.028
[139, 180] loss: 0.026
[139, 240] loss: 0.026
[139, 300] loss: 0.029
[139, 360] loss: 0.028
Epoch: 139 -> Loss: 0.0131450919434
Epoch: 139 -> Test Accuracy: 91.7675
[140, 60] loss: 0.026
[140, 120] loss: 0.025
[140, 180] loss: 0.026
[140, 240] loss: 0.030
[140, 300] loss: 0.028
[140, 360] loss: 0.028
Epoch: 140 -> Loss: 0.0177784115076
Epoch: 140 -> Test Accuracy: 91.5675
[141, 60] loss: 0.025
[141, 120] loss: 0.028
[141, 180] loss: 0.025
[141, 240] loss: 0.027
[141, 300] loss: 0.031
[141, 360] loss: 0.029
Epoch: 141 -> Loss: 0.021506762132
Epoch: 141 -> Test Accuracy: 91.7625
[142, 60] loss: 0.024
[142, 120] loss: 0.026
[142, 180] loss: 0.025
[142, 240] loss: 0.029
[142, 300] loss: 0.032
[142, 360] loss: 0.030
Epoch: 142 -> Loss: 0.03966383636
Epoch: 142 -> Test Accuracy: 91.785
[143, 60] loss: 0.026
[143, 120] loss: 0.030
[143, 180] loss: 0.028
[143, 240] loss: 0.028
[143, 300] loss: 0.028
[143, 360] loss: 0.026
Epoch: 143 -> Loss: 0.01487852633
Epoch: 143 -> Test Accuracy: 91.7
[144, 60] loss: 0.026
[144, 120] loss: 0.027
[144, 180] loss: 0.026
[144, 240] loss: 0.025
[144, 300] loss: 0.027
[144, 360] loss: 0.030
Epoch: 144 -> Loss: 0.0132517497987
Epoch: 144 -> Test Accuracy: 91.6375
[145, 60] loss: 0.028
[145, 120] loss: 0.027
[145, 180] loss: 0.025
[145, 240] loss: 0.028
[145, 300] loss: 0.029
[145, 360] loss: 0.030
Epoch: 145 -> Loss: 0.0381929054856
Epoch: 145 -> Test Accuracy: 91.5125
[146, 60] loss: 0.026
[146, 120] loss: 0.027
[146, 180] loss: 0.026
[146, 240] loss: 0.024
[146, 300] loss: 0.026
[146, 360] loss: 0.026
Epoch: 146 -> Loss: 0.0259801056236
Epoch: 146 -> Test Accuracy: 91.45
[147, 60] loss: 0.027
[147, 120] loss: 0.024
[147, 180] loss: 0.030
[147, 240] loss: 0.027
[147, 300] loss: 0.026
[147, 360] loss: 0.027
Epoch: 147 -> Loss: 0.0223569516093
Epoch: 147 -> Test Accuracy: 91.49
[148, 60] loss: 0.024
[148, 120] loss: 0.025
[148, 180] loss: 0.026
[148, 240] loss: 0.028
[148, 300] loss: 0.030
[148, 360] loss: 0.028
Epoch: 148 -> Loss: 0.0316341593862
Epoch: 148 -> Test Accuracy: 91.5475
[149, 60] loss: 0.028
[149, 120] loss: 0.027
[149, 180] loss: 0.027
[149, 240] loss: 0.027
[149, 300] loss: 0.027
[149, 360] loss: 0.030
Epoch: 149 -> Loss: 0.054685793817
Epoch: 149 -> Test Accuracy: 91.7275
[150, 60] loss: 0.027
[150, 120] loss: 0.027
[150, 180] loss: 0.027
[150, 240] loss: 0.027
[150, 300] loss: 0.029
[150, 360] loss: 0.030
Epoch: 150 -> Loss: 0.0487078540027
Epoch: 150 -> Test Accuracy: 91.6125
[151, 60] loss: 0.026
[151, 120] loss: 0.026
[151, 180] loss: 0.022
[151, 240] loss: 0.029
[151, 300] loss: 0.033
[151, 360] loss: 0.034
Epoch: 151 -> Loss: 0.0422250255942
Epoch: 151 -> Test Accuracy: 91.6625
[152, 60] loss: 0.025
[152, 120] loss: 0.024
[152, 180] loss: 0.029
[152, 240] loss: 0.028
[152, 300] loss: 0.030
[152, 360] loss: 0.030
Epoch: 152 -> Loss: 0.0139181539416
Epoch: 152 -> Test Accuracy: 91.6
[153, 60] loss: 0.026
[153, 120] loss: 0.024
[153, 180] loss: 0.026
[153, 240] loss: 0.028
[153, 300] loss: 0.026
[153, 360] loss: 0.027
Epoch: 153 -> Loss: 0.0170603133738
Epoch: 153 -> Test Accuracy: 91.595
[154, 60] loss: 0.025
[154, 120] loss: 0.030
[154, 180] loss: 0.027
[154, 240] loss: 0.029
[154, 300] loss: 0.028
[154, 360] loss: 0.030
Epoch: 154 -> Loss: 0.0775458365679
Epoch: 154 -> Test Accuracy: 91.6475
[155, 60] loss: 0.028
[155, 120] loss: 0.028
[155, 180] loss: 0.026
[155, 240] loss: 0.027
[155, 300] loss: 0.031
[155, 360] loss: 0.031
Epoch: 155 -> Loss: 0.0347133874893
Epoch: 155 -> Test Accuracy: 91.7375
[156, 60] loss: 0.030
[156, 120] loss: 0.026
[156, 180] loss: 0.029
[156, 240] loss: 0.029
[156, 300] loss: 0.030
[156, 360] loss: 0.027
Epoch: 156 -> Loss: 0.0320833846927
Epoch: 156 -> Test Accuracy: 91.77
[157, 60] loss: 0.022
[157, 120] loss: 0.027
[157, 180] loss: 0.027
[157, 240] loss: 0.028
[157, 300] loss: 0.031
[157, 360] loss: 0.030
Epoch: 157 -> Loss: 0.0669270306826
Epoch: 157 -> Test Accuracy: 91.5275
[158, 60] loss: 0.027
[158, 120] loss: 0.029
[158, 180] loss: 0.027
[158, 240] loss: 0.028
[158, 300] loss: 0.027
[158, 360] loss: 0.034
Epoch: 158 -> Loss: 0.0528500676155
Epoch: 158 -> Test Accuracy: 91.2725
[159, 60] loss: 0.027
[159, 120] loss: 0.029
[159, 180] loss: 0.027
[159, 240] loss: 0.031
[159, 300] loss: 0.028
[159, 360] loss: 0.033
Epoch: 159 -> Loss: 0.0233434811234
Epoch: 159 -> Test Accuracy: 91.66
[160, 60] loss: 0.026
[160, 120] loss: 0.027
[160, 180] loss: 0.028
[160, 240] loss: 0.029
[160, 300] loss: 0.028
[160, 360] loss: 0.030
Epoch: 160 -> Loss: 0.0234837271273
Epoch: 160 -> Test Accuracy: 91.5625
[161, 60] loss: 0.020
[161, 120] loss: 0.020
[161, 180] loss: 0.016
[161, 240] loss: 0.015
[161, 300] loss: 0.015
[161, 360] loss: 0.015
Epoch: 161 -> Loss: 0.0120748449117
Epoch: 161 -> Test Accuracy: 92.1325
[162, 60] loss: 0.013
[162, 120] loss: 0.013
[162, 180] loss: 0.012
[162, 240] loss: 0.010
[162, 300] loss: 0.012
[162, 360] loss: 0.012
Epoch: 162 -> Loss: 0.00365521688946
Epoch: 162 -> Test Accuracy: 92.1675
[163, 60] loss: 0.010
[163, 120] loss: 0.010
[163, 180] loss: 0.011
[163, 240] loss: 0.011
[163, 300] loss: 0.011
[163, 360] loss: 0.010
Epoch: 163 -> Loss: 0.00734525918961
Epoch: 163 -> Test Accuracy: 92.295
[164, 60] loss: 0.009
[164, 120] loss: 0.010
[164, 180] loss: 0.009
[164, 240] loss: 0.011
[164, 300] loss: 0.010
[164, 360] loss: 0.009
Epoch: 164 -> Loss: 0.0237358827144
Epoch: 164 -> Test Accuracy: 92.325
[165, 60] loss: 0.010
[165, 120] loss: 0.009
[165, 180] loss: 0.009
[165, 240] loss: 0.009
[165, 300] loss: 0.008
[165, 360] loss: 0.009
Epoch: 165 -> Loss: 0.0161010064185
Epoch: 165 -> Test Accuracy: 92.3025
[166, 60] loss: 0.008
[166, 120] loss: 0.008
[166, 180] loss: 0.008
[166, 240] loss: 0.009
[166, 300] loss: 0.008
[166, 360] loss: 0.009
Epoch: 166 -> Loss: 0.00349425966851
Epoch: 166 -> Test Accuracy: 92.325
[167, 60] loss: 0.007
[167, 120] loss: 0.008
[167, 180] loss: 0.007
[167, 240] loss: 0.008
[167, 300] loss: 0.008
[167, 360] loss: 0.008
Epoch: 167 -> Loss: 0.00289918854833
Epoch: 167 -> Test Accuracy: 92.4
[168, 60] loss: 0.007
[168, 120] loss: 0.007
[168, 180] loss: 0.008
[168, 240] loss: 0.007
[168, 300] loss: 0.007
[168, 360] loss: 0.007
Epoch: 168 -> Loss: 0.0039829374291
Epoch: 168 -> Test Accuracy: 92.2675
[169, 60] loss: 0.008
[169, 120] loss: 0.008
[169, 180] loss: 0.007
[169, 240] loss: 0.008
[169, 300] loss: 0.008
[169, 360] loss: 0.008
Epoch: 169 -> Loss: 0.00722488900647
Epoch: 169 -> Test Accuracy: 92.1725
[170, 60] loss: 0.007
[170, 120] loss: 0.008
[170, 180] loss: 0.008
[170, 240] loss: 0.007
[170, 300] loss: 0.008
[170, 360] loss: 0.008
Epoch: 170 -> Loss: 0.00111298705451
Epoch: 170 -> Test Accuracy: 92.29
[171, 60] loss: 0.007
[171, 120] loss: 0.006
[171, 180] loss: 0.007
[171, 240] loss: 0.007
[171, 300] loss: 0.007
[171, 360] loss: 0.006
Epoch: 171 -> Loss: 0.0110035929829
Epoch: 171 -> Test Accuracy: 92.3
[172, 60] loss: 0.007
[172, 120] loss: 0.006
[172, 180] loss: 0.006
[172, 240] loss: 0.007
[172, 300] loss: 0.007
[172, 360] loss: 0.007
Epoch: 172 -> Loss: 0.00579565856606
Epoch: 172 -> Test Accuracy: 92.31
[173, 60] loss: 0.007
[173, 120] loss: 0.006
[173, 180] loss: 0.007
[173, 240] loss: 0.008
[173, 300] loss: 0.005
[173, 360] loss: 0.007
Epoch: 173 -> Loss: 0.00440714554861
Epoch: 173 -> Test Accuracy: 92.3225
[174, 60] loss: 0.006
[174, 120] loss: 0.006
[174, 180] loss: 0.006
[174, 240] loss: 0.006
[174, 300] loss: 0.006
[174, 360] loss: 0.006
Epoch: 174 -> Loss: 0.00424929708242
Epoch: 174 -> Test Accuracy: 92.265
[175, 60] loss: 0.006
[175, 120] loss: 0.006
[175, 180] loss: 0.006
[175, 240] loss: 0.006
[175, 300] loss: 0.005
[175, 360] loss: 0.007
Epoch: 175 -> Loss: 0.0104875480756
Epoch: 175 -> Test Accuracy: 92.2475
[176, 60] loss: 0.006
[176, 120] loss: 0.006
[176, 180] loss: 0.006
[176, 240] loss: 0.006
[176, 300] loss: 0.006
[176, 360] loss: 0.006
Epoch: 176 -> Loss: 0.00488089257851
Epoch: 176 -> Test Accuracy: 92.185
[177, 60] loss: 0.006
[177, 120] loss: 0.006
[177, 180] loss: 0.006
[177, 240] loss: 0.006
[177, 300] loss: 0.005
[177, 360] loss: 0.006
Epoch: 177 -> Loss: 0.00252343830653
Epoch: 177 -> Test Accuracy: 92.2425
[178, 60] loss: 0.005
[178, 120] loss: 0.005
[178, 180] loss: 0.006
[178, 240] loss: 0.005
[178, 300] loss: 0.006
[178, 360] loss: 0.005
Epoch: 178 -> Loss: 0.00848292559385
Epoch: 178 -> Test Accuracy: 92.3325
[179, 60] loss: 0.005
[179, 120] loss: 0.006
[179, 180] loss: 0.006
[179, 240] loss: 0.006
[179, 300] loss: 0.005
[179, 360] loss: 0.006
Epoch: 179 -> Loss: 0.00370348757133
Epoch: 179 -> Test Accuracy: 92.22
[180, 60] loss: 0.005
[180, 120] loss: 0.005
[180, 180] loss: 0.006
[180, 240] loss: 0.006
[180, 300] loss: 0.006
[180, 360] loss: 0.006
Epoch: 180 -> Loss: 0.00691954931244
Epoch: 180 -> Test Accuracy: 92.2775
[181, 60] loss: 0.005
[181, 120] loss: 0.005
[181, 180] loss: 0.004
[181, 240] loss: 0.005
[181, 300] loss: 0.005
[181, 360] loss: 0.005
Epoch: 181 -> Loss: 0.00417579058558
Epoch: 181 -> Test Accuracy: 92.2975
[182, 60] loss: 0.006
[182, 120] loss: 0.006
[182, 180] loss: 0.005
[182, 240] loss: 0.005
[182, 300] loss: 0.005
[182, 360] loss: 0.005
Epoch: 182 -> Loss: 0.00415513291955
Epoch: 182 -> Test Accuracy: 92.23
[183, 60] loss: 0.005
[183, 120] loss: 0.005
[183, 180] loss: 0.005
[183, 240] loss: 0.005
[183, 300] loss: 0.005
[183, 360] loss: 0.005
Epoch: 183 -> Loss: 0.00320652802475
Epoch: 183 -> Test Accuracy: 92.2675
[184, 60] loss: 0.005
[184, 120] loss: 0.006
[184, 180] loss: 0.006
[184, 240] loss: 0.005
[184, 300] loss: 0.005
[184, 360] loss: 0.005
Epoch: 184 -> Loss: 0.00239192019217
Epoch: 184 -> Test Accuracy: 92.27
[185, 60] loss: 0.005
[185, 120] loss: 0.005
[185, 180] loss: 0.005
[185, 240] loss: 0.006
[185, 300] loss: 0.006
[185, 360] loss: 0.004
Epoch: 185 -> Loss: 0.00132351741195
Epoch: 185 -> Test Accuracy: 92.18
[186, 60] loss: 0.004
[186, 120] loss: 0.004
[186, 180] loss: 0.005
[186, 240] loss: 0.005
[186, 300] loss: 0.005
[186, 360] loss: 0.006
Epoch: 186 -> Loss: 0.00507021322846
Epoch: 186 -> Test Accuracy: 92.2875
[187, 60] loss: 0.005
[187, 120] loss: 0.005
[187, 180] loss: 0.004
[187, 240] loss: 0.006
[187, 300] loss: 0.004
[187, 360] loss: 0.005
Epoch: 187 -> Loss: 0.0131164435297
Epoch: 187 -> Test Accuracy: 92.25
[188, 60] loss: 0.005
[188, 120] loss: 0.005
[188, 180] loss: 0.005
[188, 240] loss: 0.005
[188, 300] loss: 0.005
[188, 360] loss: 0.004
Epoch: 188 -> Loss: 0.00160925020464
Epoch: 188 -> Test Accuracy: 92.2125
[189, 60] loss: 0.004
[189, 120] loss: 0.004
[189, 180] loss: 0.004
[189, 240] loss: 0.005
[189, 300] loss: 0.005
[189, 360] loss: 0.005
Epoch: 189 -> Loss: 0.00413393136114
Epoch: 189 -> Test Accuracy: 92.175
[190, 60] loss: 0.005
[190, 120] loss: 0.005
[190, 180] loss: 0.005
[190, 240] loss: 0.005
[190, 300] loss: 0.005
[190, 360] loss: 0.005
Epoch: 190 -> Loss: 0.00773674622178
Epoch: 190 -> Test Accuracy: 92.195
[191, 60] loss: 0.004
[191, 120] loss: 0.004
[191, 180] loss: 0.005
[191, 240] loss: 0.004
[191, 300] loss: 0.004
[191, 360] loss: 0.004
Epoch: 191 -> Loss: 0.00615400727838
Epoch: 191 -> Test Accuracy: 92.2125
[192, 60] loss: 0.005
[192, 120] loss: 0.005
[192, 180] loss: 0.005
[192, 240] loss: 0.004
[192, 300] loss: 0.005
[192, 360] loss: 0.005
Epoch: 192 -> Loss: 0.00324656441808
Epoch: 192 -> Test Accuracy: 92.285
[193, 60] loss: 0.004
[193, 120] loss: 0.004
[193, 180] loss: 0.005
[193, 240] loss: 0.006
[193, 300] loss: 0.005
[193, 360] loss: 0.004
Epoch: 193 -> Loss: 0.00224489858374
Epoch: 193 -> Test Accuracy: 92.225
[194, 60] loss: 0.004
[194, 120] loss: 0.005
[194, 180] loss: 0.005
[194, 240] loss: 0.005
[194, 300] loss: 0.005
[194, 360] loss: 0.004
Epoch: 194 -> Loss: 0.00926404912025
Epoch: 194 -> Test Accuracy: 92.265
[195, 60] loss: 0.005
[195, 120] loss: 0.004
[195, 180] loss: 0.005
[195, 240] loss: 0.004
[195, 300] loss: 0.005
[195, 360] loss: 0.005
Epoch: 195 -> Loss: 0.00551159540191
Epoch: 195 -> Test Accuracy: 92.165
[196, 60] loss: 0.004
[196, 120] loss: 0.005
[196, 180] loss: 0.004
[196, 240] loss: 0.005
[196, 300] loss: 0.005
[196, 360] loss: 0.005
Epoch: 196 -> Loss: 0.00217827851884
Epoch: 196 -> Test Accuracy: 92.26
[197, 60] loss: 0.004
[197, 120] loss: 0.005
[197, 180] loss: 0.005
[197, 240] loss: 0.004
[197, 300] loss: 0.004
[197, 360] loss: 0.004
Epoch: 197 -> Loss: 0.00390889961272
Epoch: 197 -> Test Accuracy: 92.23
[198, 60] loss: 0.004
[198, 120] loss: 0.005
[198, 180] loss: 0.004
[198, 240] loss: 0.005
[198, 300] loss: 0.005
[198, 360] loss: 0.004
Epoch: 198 -> Loss: 0.0150258438662
Epoch: 198 -> Test Accuracy: 92.2525
[199, 60] loss: 0.004
[199, 120] loss: 0.005
[199, 180] loss: 0.004
[199, 240] loss: 0.005
[199, 300] loss: 0.004
[199, 360] loss: 0.004
Epoch: 199 -> Loss: 0.00687549123541
Epoch: 199 -> Test Accuracy: 92.2475
[200, 60] loss: 0.004
[200, 120] loss: 0.005
[200, 180] loss: 0.005
[200, 240] loss: 0.004
[200, 300] loss: 0.004
[200, 360] loss: 0.004
Epoch: 200 -> Loss: 0.00446217041463
Epoch: 200 -> Test Accuracy: 92.225
Finished Training
In [8]:
# train NonLinearClassifiers on feature map of net_3block
block5_loss_log, _, block5_test_accuracy_log, _, _ = tr.train_all_blocks(5, 10, [0.1, 0.02, 0.004, 0.0008], 
    [20, 40, 45, 100], 0.9, 5e-4, net_block5, criterion, trainloader, None, testloader) 
[1, 60] loss: 2.206
[1, 120] loss: 1.259
[1, 180] loss: 1.152
[1, 240] loss: 1.113
[1, 300] loss: 1.040
[1, 360] loss: 1.005
Epoch: 1 -> Loss: 0.989844799042
Epoch: 1 -> Test Accuracy: 66.68
[2, 60] loss: 0.940
[2, 120] loss: 0.934
[2, 180] loss: 0.915
[2, 240] loss: 0.924
[2, 300] loss: 0.895
[2, 360] loss: 0.877
Epoch: 2 -> Loss: 0.998141288757
Epoch: 2 -> Test Accuracy: 71.31
[3, 60] loss: 0.845
[3, 120] loss: 0.839
[3, 180] loss: 0.833
[3, 240] loss: 0.820
[3, 300] loss: 0.793
[3, 360] loss: 0.805
Epoch: 3 -> Loss: 1.01133608818
Epoch: 3 -> Test Accuracy: 72.87
[4, 60] loss: 0.778
[4, 120] loss: 0.788
[4, 180] loss: 0.780
[4, 240] loss: 0.791
[4, 300] loss: 0.778
[4, 360] loss: 0.751
Epoch: 4 -> Loss: 0.792004227638
Epoch: 4 -> Test Accuracy: 73.82
[5, 60] loss: 0.761
[5, 120] loss: 0.749
[5, 180] loss: 0.750
[5, 240] loss: 0.741
[5, 300] loss: 0.738
[5, 360] loss: 0.718
Epoch: 5 -> Loss: 0.875502228737
Epoch: 5 -> Test Accuracy: 75.2
[6, 60] loss: 0.703
[6, 120] loss: 0.722
[6, 180] loss: 0.706
[6, 240] loss: 0.724
[6, 300] loss: 0.739
[6, 360] loss: 0.707
Epoch: 6 -> Loss: 0.882703483105
Epoch: 6 -> Test Accuracy: 75.69
[7, 60] loss: 0.675
[7, 120] loss: 0.677
[7, 180] loss: 0.704
[7, 240] loss: 0.685
[7, 300] loss: 0.726
[7, 360] loss: 0.681
Epoch: 7 -> Loss: 0.826486110687
Epoch: 7 -> Test Accuracy: 76.09
[8, 60] loss: 0.669
[8, 120] loss: 0.690
[8, 180] loss: 0.668
[8, 240] loss: 0.692
[8, 300] loss: 0.680
[8, 360] loss: 0.707
Epoch: 8 -> Loss: 0.748009562492
Epoch: 8 -> Test Accuracy: 76.21
[9, 60] loss: 0.679
[9, 120] loss: 0.666
[9, 180] loss: 0.657
[9, 240] loss: 0.680
[9, 300] loss: 0.684
[9, 360] loss: 0.688
Epoch: 9 -> Loss: 0.712456583977
Epoch: 9 -> Test Accuracy: 76.75
[10, 60] loss: 0.639
[10, 120] loss: 0.660
[10, 180] loss: 0.652
[10, 240] loss: 0.677
[10, 300] loss: 0.651
[10, 360] loss: 0.679
Epoch: 10 -> Loss: 0.672299444675
Epoch: 10 -> Test Accuracy: 77.05
[11, 60] loss: 0.652
[11, 120] loss: 0.665
[11, 180] loss: 0.662
[11, 240] loss: 0.656
[11, 300] loss: 0.642
[11, 360] loss: 0.661
Epoch: 11 -> Loss: 0.651005268097
Epoch: 11 -> Test Accuracy: 77.09
[12, 60] loss: 0.643
[12, 120] loss: 0.640
[12, 180] loss: 0.648
[12, 240] loss: 0.661
[12, 300] loss: 0.644
[12, 360] loss: 0.644
Epoch: 12 -> Loss: 0.640797972679
Epoch: 12 -> Test Accuracy: 76.95
[13, 60] loss: 0.635
[13, 120] loss: 0.621
[13, 180] loss: 0.653
[13, 240] loss: 0.632
[13, 300] loss: 0.644
[13, 360] loss: 0.651
Epoch: 13 -> Loss: 0.503665864468
Epoch: 13 -> Test Accuracy: 77.29
[14, 60] loss: 0.628
[14, 120] loss: 0.637
[14, 180] loss: 0.661
[14, 240] loss: 0.631
[14, 300] loss: 0.651
[14, 360] loss: 0.654
Epoch: 14 -> Loss: 0.545984447002
Epoch: 14 -> Test Accuracy: 77.82
[15, 60] loss: 0.595
[15, 120] loss: 0.633
[15, 180] loss: 0.631
[15, 240] loss: 0.650
[15, 300] loss: 0.637
[15, 360] loss: 0.642
Epoch: 15 -> Loss: 0.540837943554
Epoch: 15 -> Test Accuracy: 77.17
[16, 60] loss: 0.609
[16, 120] loss: 0.637
[16, 180] loss: 0.632
[16, 240] loss: 0.634
[16, 300] loss: 0.634
[16, 360] loss: 0.639
Epoch: 16 -> Loss: 0.786884486675
Epoch: 16 -> Test Accuracy: 77.27
[17, 60] loss: 0.599
[17, 120] loss: 0.627
[17, 180] loss: 0.609
[17, 240] loss: 0.634
[17, 300] loss: 0.649
[17, 360] loss: 0.640
Epoch: 17 -> Loss: 0.655207037926
Epoch: 17 -> Test Accuracy: 78.05
[18, 60] loss: 0.621
[18, 120] loss: 0.629
[18, 180] loss: 0.619
[18, 240] loss: 0.626
[18, 300] loss: 0.620
[18, 360] loss: 0.634
Epoch: 18 -> Loss: 0.673180103302
Epoch: 18 -> Test Accuracy: 77.42
[19, 60] loss: 0.612
[19, 120] loss: 0.615
[19, 180] loss: 0.627
[19, 240] loss: 0.600
[19, 300] loss: 0.629
[19, 360] loss: 0.620
Epoch: 19 -> Loss: 0.856933951378
Epoch: 19 -> Test Accuracy: 77.78
[20, 60] loss: 0.615
[20, 120] loss: 0.616
[20, 180] loss: 0.614
[20, 240] loss: 0.618
[20, 300] loss: 0.624
[20, 360] loss: 0.638
Epoch: 20 -> Loss: 0.642349123955
Epoch: 20 -> Test Accuracy: 77.8
[21, 60] loss: 0.555
[21, 120] loss: 0.550
[21, 180] loss: 0.512
[21, 240] loss: 0.516
[21, 300] loss: 0.510
[21, 360] loss: 0.518
Epoch: 21 -> Loss: 0.472536504269
Epoch: 21 -> Test Accuracy: 80.23
[22, 60] loss: 0.479
[22, 120] loss: 0.500
[22, 180] loss: 0.479
[22, 240] loss: 0.474
[22, 300] loss: 0.465
[22, 360] loss: 0.484
Epoch: 22 -> Loss: 0.399843990803
Epoch: 22 -> Test Accuracy: 80.83
[23, 60] loss: 0.482
[23, 120] loss: 0.454
[23, 180] loss: 0.453
[23, 240] loss: 0.465
[23, 300] loss: 0.465
[23, 360] loss: 0.461
Epoch: 23 -> Loss: 0.571590662003
Epoch: 23 -> Test Accuracy: 80.95
[24, 60] loss: 0.448
[24, 120] loss: 0.434
[24, 180] loss: 0.455
[24, 240] loss: 0.460
[24, 300] loss: 0.478
[24, 360] loss: 0.456
Epoch: 24 -> Loss: 0.458548158407
Epoch: 24 -> Test Accuracy: 81.19
[25, 60] loss: 0.441
[25, 120] loss: 0.441
[25, 180] loss: 0.447
[25, 240] loss: 0.449
[25, 300] loss: 0.429
[25, 360] loss: 0.461
Epoch: 25 -> Loss: 0.461026012897
Epoch: 25 -> Test Accuracy: 81.26
[26, 60] loss: 0.434
[26, 120] loss: 0.428
[26, 180] loss: 0.441
[26, 240] loss: 0.445
[26, 300] loss: 0.441
[26, 360] loss: 0.444
Epoch: 26 -> Loss: 0.455417454243
Epoch: 26 -> Test Accuracy: 81.19
[27, 60] loss: 0.427
[27, 120] loss: 0.425
[27, 180] loss: 0.449
[27, 240] loss: 0.437
[27, 300] loss: 0.447
[27, 360] loss: 0.423
Epoch: 27 -> Loss: 0.410543859005
Epoch: 27 -> Test Accuracy: 81.97
[28, 60] loss: 0.415
[28, 120] loss: 0.419
[28, 180] loss: 0.420
[28, 240] loss: 0.427
[28, 300] loss: 0.438
[28, 360] loss: 0.443
Epoch: 28 -> Loss: 0.532772958279
Epoch: 28 -> Test Accuracy: 81.37
[29, 60] loss: 0.418
[29, 120] loss: 0.407
[29, 180] loss: 0.406
[29, 240] loss: 0.414
[29, 300] loss: 0.427
[29, 360] loss: 0.426
Epoch: 29 -> Loss: 0.433989435434
Epoch: 29 -> Test Accuracy: 81.3
[30, 60] loss: 0.421
[30, 120] loss: 0.411
[30, 180] loss: 0.405
[30, 240] loss: 0.435
[30, 300] loss: 0.411
[30, 360] loss: 0.434
Epoch: 30 -> Loss: 0.345436096191
Epoch: 30 -> Test Accuracy: 81.55
[31, 60] loss: 0.396
[31, 120] loss: 0.426
[31, 180] loss: 0.427
[31, 240] loss: 0.425
[31, 300] loss: 0.418
[31, 360] loss: 0.418
Epoch: 31 -> Loss: 0.379749000072
Epoch: 31 -> Test Accuracy: 80.96
[32, 60] loss: 0.414
[32, 120] loss: 0.400
[32, 180] loss: 0.406
[32, 240] loss: 0.426
[32, 300] loss: 0.430
[32, 360] loss: 0.429
Epoch: 32 -> Loss: 0.340396940708
Epoch: 32 -> Test Accuracy: 80.91
[33, 60] loss: 0.405
[33, 120] loss: 0.415
[33, 180] loss: 0.428
[33, 240] loss: 0.408
[33, 300] loss: 0.405
[33, 360] loss: 0.421
Epoch: 33 -> Loss: 0.339397251606
Epoch: 33 -> Test Accuracy: 81.32
[34, 60] loss: 0.397
[34, 120] loss: 0.406
[34, 180] loss: 0.411
[34, 240] loss: 0.419
[34, 300] loss: 0.424
[34, 360] loss: 0.414
Epoch: 34 -> Loss: 0.495242774487
Epoch: 34 -> Test Accuracy: 81.44
[35, 60] loss: 0.380
[35, 120] loss: 0.419
[35, 180] loss: 0.411
[35, 240] loss: 0.409
[35, 300] loss: 0.420
[35, 360] loss: 0.406
Epoch: 35 -> Loss: 0.382247358561
Epoch: 35 -> Test Accuracy: 81.48
[36, 60] loss: 0.409
[36, 120] loss: 0.420
[36, 180] loss: 0.419
[36, 240] loss: 0.398
[36, 300] loss: 0.419
[36, 360] loss: 0.429
Epoch: 36 -> Loss: 0.416755497456
Epoch: 36 -> Test Accuracy: 81.15
[37, 60] loss: 0.407
[37, 120] loss: 0.409
[37, 180] loss: 0.402
[37, 240] loss: 0.401
[37, 300] loss: 0.401
[37, 360] loss: 0.433
Epoch: 37 -> Loss: 0.355153858662
Epoch: 37 -> Test Accuracy: 80.79
[38, 60] loss: 0.402
[38, 120] loss: 0.395
[38, 180] loss: 0.401
[38, 240] loss: 0.403
[38, 300] loss: 0.405
[38, 360] loss: 0.410
Epoch: 38 -> Loss: 0.381116777658
Epoch: 38 -> Test Accuracy: 80.63
[39, 60] loss: 0.398
[39, 120] loss: 0.408
[39, 180] loss: 0.409
[39, 240] loss: 0.397
[39, 300] loss: 0.409
[39, 360] loss: 0.438
Epoch: 39 -> Loss: 0.557063698769
Epoch: 39 -> Test Accuracy: 80.85
[40, 60] loss: 0.396
[40, 120] loss: 0.403
[40, 180] loss: 0.398
[40, 240] loss: 0.407
[40, 300] loss: 0.424
[40, 360] loss: 0.421
Epoch: 40 -> Loss: 0.344882249832
Epoch: 40 -> Test Accuracy: 81.3
[41, 60] loss: 0.384
[41, 120] loss: 0.367
[41, 180] loss: 0.370
[41, 240] loss: 0.342
[41, 300] loss: 0.361
[41, 360] loss: 0.352
Epoch: 41 -> Loss: 0.472389042377
Epoch: 41 -> Test Accuracy: 82.12
[42, 60] loss: 0.338
[42, 120] loss: 0.332
[42, 180] loss: 0.332
[42, 240] loss: 0.342
[42, 300] loss: 0.346
[42, 360] loss: 0.336
Epoch: 42 -> Loss: 0.429946184158
Epoch: 42 -> Test Accuracy: 82.26
[43, 60] loss: 0.325
[43, 120] loss: 0.326
[43, 180] loss: 0.332
[43, 240] loss: 0.324
[43, 300] loss: 0.325
[43, 360] loss: 0.335
Epoch: 43 -> Loss: 0.315294861794
Epoch: 43 -> Test Accuracy: 82.13
[44, 60] loss: 0.317
[44, 120] loss: 0.343
[44, 180] loss: 0.316
[44, 240] loss: 0.322
[44, 300] loss: 0.321
[44, 360] loss: 0.320
Epoch: 44 -> Loss: 0.436052948236
Epoch: 44 -> Test Accuracy: 82.21
[45, 60] loss: 0.323
[45, 120] loss: 0.308
[45, 180] loss: 0.323
[45, 240] loss: 0.324
[45, 300] loss: 0.307
[45, 360] loss: 0.319
Epoch: 45 -> Loss: 0.373412132263
Epoch: 45 -> Test Accuracy: 82.3
[46, 60] loss: 0.299
[46, 120] loss: 0.298
[46, 180] loss: 0.313
[46, 240] loss: 0.297
[46, 300] loss: 0.296
[46, 360] loss: 0.312
Epoch: 46 -> Loss: 0.242745012045
Epoch: 46 -> Test Accuracy: 82.47
[47, 60] loss: 0.293
[47, 120] loss: 0.297
[47, 180] loss: 0.295
[47, 240] loss: 0.305
[47, 300] loss: 0.301
[47, 360] loss: 0.304
Epoch: 47 -> Loss: 0.337700366974
Epoch: 47 -> Test Accuracy: 82.36
[48, 60] loss: 0.295
[48, 120] loss: 0.297
[48, 180] loss: 0.295
[48, 240] loss: 0.297
[48, 300] loss: 0.291
[48, 360] loss: 0.293
Epoch: 48 -> Loss: 0.408868640661
Epoch: 48 -> Test Accuracy: 82.4
[49, 60] loss: 0.294
[49, 120] loss: 0.305
[49, 180] loss: 0.293
[49, 240] loss: 0.301
[49, 300] loss: 0.294
[49, 360] loss: 0.288
Epoch: 49 -> Loss: 0.565850496292
Epoch: 49 -> Test Accuracy: 82.51
[50, 60] loss: 0.298
[50, 120] loss: 0.282
[50, 180] loss: 0.291
[50, 240] loss: 0.299
[50, 300] loss: 0.305
[50, 360] loss: 0.296
Epoch: 50 -> Loss: 0.328209519386
Epoch: 50 -> Test Accuracy: 82.43
[51, 60] loss: 0.279
[51, 120] loss: 0.292
[51, 180] loss: 0.298
[51, 240] loss: 0.283
[51, 300] loss: 0.287
[51, 360] loss: 0.300
Epoch: 51 -> Loss: 0.325222551823
Epoch: 51 -> Test Accuracy: 82.38
[52, 60] loss: 0.282
[52, 120] loss: 0.280
[52, 180] loss: 0.278
[52, 240] loss: 0.294
[52, 300] loss: 0.302
[52, 360] loss: 0.304
Epoch: 52 -> Loss: 0.353080123663
Epoch: 52 -> Test Accuracy: 82.46
[53, 60] loss: 0.274
[53, 120] loss: 0.287
[53, 180] loss: 0.280
[53, 240] loss: 0.304
[53, 300] loss: 0.289
[53, 360] loss: 0.277
Epoch: 53 -> Loss: 0.293329536915
Epoch: 53 -> Test Accuracy: 82.55
[54, 60] loss: 0.294
[54, 120] loss: 0.284
[54, 180] loss: 0.278
[54, 240] loss: 0.285
[54, 300] loss: 0.288
[54, 360] loss: 0.298
Epoch: 54 -> Loss: 0.22264918685
Epoch: 54 -> Test Accuracy: 82.44
[55, 60] loss: 0.296
[55, 120] loss: 0.291
[55, 180] loss: 0.287
[55, 240] loss: 0.289
[55, 300] loss: 0.275
[55, 360] loss: 0.275
Epoch: 55 -> Loss: 0.552338838577
Epoch: 55 -> Test Accuracy: 82.44
[56, 60] loss: 0.286
[56, 120] loss: 0.281
[56, 180] loss: 0.280
[56, 240] loss: 0.284
[56, 300] loss: 0.286
[56, 360] loss: 0.279
Epoch: 56 -> Loss: 0.26289281249
Epoch: 56 -> Test Accuracy: 82.51
[57, 60] loss: 0.270
[57, 120] loss: 0.276
[57, 180] loss: 0.276
[57, 240] loss: 0.287
[57, 300] loss: 0.291
[57, 360] loss: 0.282
Epoch: 57 -> Loss: 0.189671799541
Epoch: 57 -> Test Accuracy: 82.6
[58, 60] loss: 0.290
[58, 120] loss: 0.264
[58, 180] loss: 0.285
[58, 240] loss: 0.287
[58, 300] loss: 0.290
[58, 360] loss: 0.278
Epoch: 58 -> Loss: 0.243798166513
Epoch: 58 -> Test Accuracy: 82.53
[59, 60] loss: 0.279
[59, 120] loss: 0.285
[59, 180] loss: 0.275
[59, 240] loss: 0.289
[59, 300] loss: 0.287
[59, 360] loss: 0.283
Epoch: 59 -> Loss: 0.253309726715
Epoch: 59 -> Test Accuracy: 82.5
[60, 60] loss: 0.287
[60, 120] loss: 0.279
[60, 180] loss: 0.272
[60, 240] loss: 0.277
[60, 300] loss: 0.269
[60, 360] loss: 0.270
Epoch: 60 -> Loss: 0.283162623644
Epoch: 60 -> Test Accuracy: 82.51
[61, 60] loss: 0.274
[61, 120] loss: 0.273
[61, 180] loss: 0.286
[61, 240] loss: 0.275
[61, 300] loss: 0.272
[61, 360] loss: 0.285
Epoch: 61 -> Loss: 0.313937842846
Epoch: 61 -> Test Accuracy: 82.63
[62, 60] loss: 0.283
[62, 120] loss: 0.274
[62, 180] loss: 0.262
[62, 240] loss: 0.280
[62, 300] loss: 0.275
[62, 360] loss: 0.283
Epoch: 62 -> Loss: 0.335236012936
Epoch: 62 -> Test Accuracy: 82.52
[63, 60] loss: 0.285
[63, 120] loss: 0.277
[63, 180] loss: 0.279
[63, 240] loss: 0.264
[63, 300] loss: 0.277
[63, 360] loss: 0.278
Epoch: 63 -> Loss: 0.311462879181
Epoch: 63 -> Test Accuracy: 82.52
[64, 60] loss: 0.271
[64, 120] loss: 0.272
[64, 180] loss: 0.273
[64, 240] loss: 0.268
[64, 300] loss: 0.290
[64, 360] loss: 0.279
Epoch: 64 -> Loss: 0.222185850143
Epoch: 64 -> Test Accuracy: 82.72
[65, 60] loss: 0.269
[65, 120] loss: 0.286
[65, 180] loss: 0.276
[65, 240] loss: 0.285
[65, 300] loss: 0.275
[65, 360] loss: 0.262
Epoch: 65 -> Loss: 0.18132416904
Epoch: 65 -> Test Accuracy: 82.74
[66, 60] loss: 0.266
[66, 120] loss: 0.272
[66, 180] loss: 0.267
[66, 240] loss: 0.273
[66, 300] loss: 0.279
[66, 360] loss: 0.265
Epoch: 66 -> Loss: 0.226240590215
Epoch: 66 -> Test Accuracy: 82.53
[67, 60] loss: 0.276
[67, 120] loss: 0.251
[67, 180] loss: 0.271
[67, 240] loss: 0.262
[67, 300] loss: 0.277
[67, 360] loss: 0.269
Epoch: 67 -> Loss: 0.19538384676
Epoch: 67 -> Test Accuracy: 82.62
[68, 60] loss: 0.276
[68, 120] loss: 0.273
[68, 180] loss: 0.271
[68, 240] loss: 0.270
[68, 300] loss: 0.268
[68, 360] loss: 0.263
Epoch: 68 -> Loss: 0.344725430012
Epoch: 68 -> Test Accuracy: 82.66
[69, 60] loss: 0.270
[69, 120] loss: 0.260
[69, 180] loss: 0.274
[69, 240] loss: 0.269
[69, 300] loss: 0.264
[69, 360] loss: 0.258
Epoch: 69 -> Loss: 0.245025873184
Epoch: 69 -> Test Accuracy: 82.71
[70, 60] loss: 0.271
[70, 120] loss: 0.267
[70, 180] loss: 0.269
[70, 240] loss: 0.281
[70, 300] loss: 0.257
[70, 360] loss: 0.275
Epoch: 70 -> Loss: 0.29664465785
Epoch: 70 -> Test Accuracy: 82.57
[71, 60] loss: 0.265
[71, 120] loss: 0.266
[71, 180] loss: 0.264
[71, 240] loss: 0.262
[71, 300] loss: 0.262
[71, 360] loss: 0.273
Epoch: 71 -> Loss: 0.216807082295
Epoch: 71 -> Test Accuracy: 82.67
[72, 60] loss: 0.264
[72, 120] loss: 0.267
[72, 180] loss: 0.264
[72, 240] loss: 0.261
[72, 300] loss: 0.274
[72, 360] loss: 0.274
Epoch: 72 -> Loss: 0.3693177104
Epoch: 72 -> Test Accuracy: 82.4
[73, 60] loss: 0.275
[73, 120] loss: 0.255
[73, 180] loss: 0.269
[73, 240] loss: 0.264
[73, 300] loss: 0.257
[73, 360] loss: 0.270
Epoch: 73 -> Loss: 0.250025957823
Epoch: 73 -> Test Accuracy: 82.48
[74, 60] loss: 0.260
[74, 120] loss: 0.273
[74, 180] loss: 0.266
[74, 240] loss: 0.258
[74, 300] loss: 0.261
[74, 360] loss: 0.263
Epoch: 74 -> Loss: 0.241463631392
Epoch: 74 -> Test Accuracy: 82.53
[75, 60] loss: 0.257
[75, 120] loss: 0.263
[75, 180] loss: 0.270
[75, 240] loss: 0.270
[75, 300] loss: 0.253
[75, 360] loss: 0.265
Epoch: 75 -> Loss: 0.297407001257
Epoch: 75 -> Test Accuracy: 82.38
[76, 60] loss: 0.245
[76, 120] loss: 0.259
[76, 180] loss: 0.278
[76, 240] loss: 0.260
[76, 300] loss: 0.276
[76, 360] loss: 0.273
Epoch: 76 -> Loss: 0.329516738653
Epoch: 76 -> Test Accuracy: 82.29
[77, 60] loss: 0.257
[77, 120] loss: 0.256
[77, 180] loss: 0.270
[77, 240] loss: 0.257
[77, 300] loss: 0.258
[77, 360] loss: 0.260
Epoch: 77 -> Loss: 0.222680807114
Epoch: 77 -> Test Accuracy: 82.39
[78, 60] loss: 0.262
[78, 120] loss: 0.265
[78, 180] loss: 0.267
[78, 240] loss: 0.254
[78, 300] loss: 0.261
[78, 360] loss: 0.263
Epoch: 78 -> Loss: 0.245288655162
Epoch: 78 -> Test Accuracy: 82.49
[79, 60] loss: 0.257
[79, 120] loss: 0.259
[79, 180] loss: 0.259
[79, 240] loss: 0.254
[79, 300] loss: 0.266
[79, 360] loss: 0.275
Epoch: 79 -> Loss: 0.277887523174
Epoch: 79 -> Test Accuracy: 82.54
[80, 60] loss: 0.260
[80, 120] loss: 0.261
[80, 180] loss: 0.260
[80, 240] loss: 0.260
[80, 300] loss: 0.261
[80, 360] loss: 0.258
Epoch: 80 -> Loss: 0.214941501617
Epoch: 80 -> Test Accuracy: 82.62
[81, 60] loss: 0.264
[81, 120] loss: 0.264
[81, 180] loss: 0.259
[81, 240] loss: 0.257
[81, 300] loss: 0.254
[81, 360] loss: 0.267
Epoch: 81 -> Loss: 0.219567447901
Epoch: 81 -> Test Accuracy: 82.65
[82, 60] loss: 0.259
[82, 120] loss: 0.254
[82, 180] loss: 0.255
[82, 240] loss: 0.257
[82, 300] loss: 0.265
[82, 360] loss: 0.253
Epoch: 82 -> Loss: 0.262077003717
Epoch: 82 -> Test Accuracy: 82.56
[83, 60] loss: 0.254
[83, 120] loss: 0.270
[83, 180] loss: 0.262
[83, 240] loss: 0.248
[83, 300] loss: 0.262
[83, 360] loss: 0.255
Epoch: 83 -> Loss: 0.187527015805
Epoch: 83 -> Test Accuracy: 82.52
[84, 60] loss: 0.244
[84, 120] loss: 0.251
[84, 180] loss: 0.251
[84, 240] loss: 0.259
[84, 300] loss: 0.267
[84, 360] loss: 0.263
Epoch: 84 -> Loss: 0.235802292824
Epoch: 84 -> Test Accuracy: 82.6
[85, 60] loss: 0.251
[85, 120] loss: 0.248
[85, 180] loss: 0.251
[85, 240] loss: 0.255
[85, 300] loss: 0.259
[85, 360] loss: 0.244
Epoch: 85 -> Loss: 0.219255179167
Epoch: 85 -> Test Accuracy: 82.75
[86, 60] loss: 0.257
[86, 120] loss: 0.253
[86, 180] loss: 0.261
[86, 240] loss: 0.248
[86, 300] loss: 0.247
[86, 360] loss: 0.259
Epoch: 86 -> Loss: 0.169390812516
Epoch: 86 -> Test Accuracy: 82.82
[87, 60] loss: 0.267
[87, 120] loss: 0.256
[87, 180] loss: 0.249
[87, 240] loss: 0.249
[87, 300] loss: 0.252
[87, 360] loss: 0.252
Epoch: 87 -> Loss: 0.29005163908
Epoch: 87 -> Test Accuracy: 82.6
[88, 60] loss: 0.257
[88, 120] loss: 0.257
[88, 180] loss: 0.247
[88, 240] loss: 0.243
[88, 300] loss: 0.249
[88, 360] loss: 0.254
Epoch: 88 -> Loss: 0.368238866329
Epoch: 88 -> Test Accuracy: 82.58
[89, 60] loss: 0.253
[89, 120] loss: 0.267
[89, 180] loss: 0.243
[89, 240] loss: 0.254
[89, 300] loss: 0.256
[89, 360] loss: 0.253
Epoch: 89 -> Loss: 0.257168114185
Epoch: 89 -> Test Accuracy: 82.64
[90, 60] loss: 0.254
[90, 120] loss: 0.251
[90, 180] loss: 0.239
[90, 240] loss: 0.258
[90, 300] loss: 0.249
[90, 360] loss: 0.259
Epoch: 90 -> Loss: 0.238972112536
Epoch: 90 -> Test Accuracy: 82.66
[91, 60] loss: 0.243
[91, 120] loss: 0.253
[91, 180] loss: 0.246
[91, 240] loss: 0.253
[91, 300] loss: 0.256
[91, 360] loss: 0.249
Epoch: 91 -> Loss: 0.373318344355
Epoch: 91 -> Test Accuracy: 82.67
[92, 60] loss: 0.247
[92, 120] loss: 0.256
[92, 180] loss: 0.244
[92, 240] loss: 0.251
[92, 300] loss: 0.247
[92, 360] loss: 0.250
Epoch: 92 -> Loss: 0.139528647065
Epoch: 92 -> Test Accuracy: 82.53
[93, 60] loss: 0.244
[93, 120] loss: 0.238
[93, 180] loss: 0.250
[93, 240] loss: 0.251
[93, 300] loss: 0.252
[93, 360] loss: 0.245
Epoch: 93 -> Loss: 0.251868247986
Epoch: 93 -> Test Accuracy: 82.57
[94, 60] loss: 0.244
[94, 120] loss: 0.251
[94, 180] loss: 0.242
[94, 240] loss: 0.243
[94, 300] loss: 0.244
[94, 360] loss: 0.264
Epoch: 94 -> Loss: 0.244362637401
Epoch: 94 -> Test Accuracy: 82.45
[95, 60] loss: 0.244
[95, 120] loss: 0.252
[95, 180] loss: 0.238
[95, 240] loss: 0.241
[95, 300] loss: 0.248
[95, 360] loss: 0.255
Epoch: 95 -> Loss: 0.222351387143
Epoch: 95 -> Test Accuracy: 82.59
[96, 60] loss: 0.241
[96, 120] loss: 0.242
[96, 180] loss: 0.235
[96, 240] loss: 0.248
[96, 300] loss: 0.248
[96, 360] loss: 0.251
Epoch: 96 -> Loss: 0.273922264576
Epoch: 96 -> Test Accuracy: 82.48
[97, 60] loss: 0.239
[97, 120] loss: 0.250
[97, 180] loss: 0.255
[97, 240] loss: 0.241
[97, 300] loss: 0.244
[97, 360] loss: 0.246
Epoch: 97 -> Loss: 0.318357616663
Epoch: 97 -> Test Accuracy: 82.69
[98, 60] loss: 0.244
[98, 120] loss: 0.247
[98, 180] loss: 0.242
[98, 240] loss: 0.240
[98, 300] loss: 0.244
[98, 360] loss: 0.234
Epoch: 98 -> Loss: 0.209844306111
Epoch: 98 -> Test Accuracy: 82.58
[99, 60] loss: 0.233
[99, 120] loss: 0.234
[99, 180] loss: 0.233
[99, 240] loss: 0.253
[99, 300] loss: 0.259
[99, 360] loss: 0.249
Epoch: 99 -> Loss: 0.286715209484
Epoch: 99 -> Test Accuracy: 82.71
[100, 60] loss: 0.242
[100, 120] loss: 0.253
[100, 180] loss: 0.252
[100, 240] loss: 0.238
[100, 300] loss: 0.251
[100, 360] loss: 0.237
Epoch: 100 -> Loss: 0.240406125784
Epoch: 100 -> Test Accuracy: 82.64
Finished Training
[1, 60] loss: 1.745
[1, 120] loss: 0.841
[1, 180] loss: 0.763
[1, 240] loss: 0.717
[1, 300] loss: 0.704
[1, 360] loss: 0.660
Epoch: 1 -> Loss: 0.522156357765
Epoch: 1 -> Test Accuracy: 77.76
[2, 60] loss: 0.598
[2, 120] loss: 0.610
[2, 180] loss: 0.584
[2, 240] loss: 0.577
[2, 300] loss: 0.562
[2, 360] loss: 0.563
Epoch: 2 -> Loss: 0.696018874645
Epoch: 2 -> Test Accuracy: 80.68
[3, 60] loss: 0.511
[3, 120] loss: 0.534
[3, 180] loss: 0.516
[3, 240] loss: 0.514
[3, 300] loss: 0.521
[3, 360] loss: 0.526
Epoch: 3 -> Loss: 0.558457434177
Epoch: 3 -> Test Accuracy: 81.52
[4, 60] loss: 0.475
[4, 120] loss: 0.470
[4, 180] loss: 0.496
[4, 240] loss: 0.487
[4, 300] loss: 0.489
[4, 360] loss: 0.489
Epoch: 4 -> Loss: 0.476736158133
Epoch: 4 -> Test Accuracy: 82.34
[5, 60] loss: 0.460
[5, 120] loss: 0.463
[5, 180] loss: 0.449
[5, 240] loss: 0.465
[5, 300] loss: 0.471
[5, 360] loss: 0.469
Epoch: 5 -> Loss: 0.391936689615
Epoch: 5 -> Test Accuracy: 82.78
[6, 60] loss: 0.422
[6, 120] loss: 0.439
[6, 180] loss: 0.446
[6, 240] loss: 0.453
[6, 300] loss: 0.440
[6, 360] loss: 0.438
Epoch: 6 -> Loss: 0.426587998867
Epoch: 6 -> Test Accuracy: 83.13
[7, 60] loss: 0.409
[7, 120] loss: 0.443
[7, 180] loss: 0.433
[7, 240] loss: 0.438
[7, 300] loss: 0.426
[7, 360] loss: 0.437
Epoch: 7 -> Loss: 0.472709596157
Epoch: 7 -> Test Accuracy: 83.3
[8, 60] loss: 0.402
[8, 120] loss: 0.413
[8, 180] loss: 0.437
[8, 240] loss: 0.416
[8, 300] loss: 0.433
[8, 360] loss: 0.432
Epoch: 8 -> Loss: 0.505204975605
Epoch: 8 -> Test Accuracy: 83.45
[9, 60] loss: 0.390
[9, 120] loss: 0.413
[9, 180] loss: 0.426
[9, 240] loss: 0.412
[9, 300] loss: 0.414
[9, 360] loss: 0.417
Epoch: 9 -> Loss: 0.393939316273
Epoch: 9 -> Test Accuracy: 82.76
[10, 60] loss: 0.412
[10, 120] loss: 0.396
[10, 180] loss: 0.405
[10, 240] loss: 0.398
[10, 300] loss: 0.427
[10, 360] loss: 0.421
Epoch: 10 -> Loss: 0.536645770073
Epoch: 10 -> Test Accuracy: 83.96
[11, 60] loss: 0.393
[11, 120] loss: 0.374
[11, 180] loss: 0.423
[11, 240] loss: 0.394
[11, 300] loss: 0.418
[11, 360] loss: 0.407
Epoch: 11 -> Loss: 0.675051510334
Epoch: 11 -> Test Accuracy: 83.22
[12, 60] loss: 0.370
[12, 120] loss: 0.392
[12, 180] loss: 0.391
[12, 240] loss: 0.402
[12, 300] loss: 0.399
[12, 360] loss: 0.399
Epoch: 12 -> Loss: 0.475282132626
Epoch: 12 -> Test Accuracy: 83.68
[13, 60] loss: 0.375
[13, 120] loss: 0.386
[13, 180] loss: 0.395
[13, 240] loss: 0.390
[13, 300] loss: 0.417
[13, 360] loss: 0.397
Epoch: 13 -> Loss: 0.470419168472
Epoch: 13 -> Test Accuracy: 83.8
[14, 60] loss: 0.367
[14, 120] loss: 0.373
[14, 180] loss: 0.387
[14, 240] loss: 0.399
[14, 300] loss: 0.400
[14, 360] loss: 0.403
Epoch: 14 -> Loss: 0.278404980898
Epoch: 14 -> Test Accuracy: 83.76
[15, 60] loss: 0.372
[15, 120] loss: 0.375
[15, 180] loss: 0.376
[15, 240] loss: 0.384
[15, 300] loss: 0.406
[15, 360] loss: 0.399
Epoch: 15 -> Loss: 0.458360135555
Epoch: 15 -> Test Accuracy: 83.84
[16, 60] loss: 0.350
[16, 120] loss: 0.382
[16, 180] loss: 0.404
[16, 240] loss: 0.370
[16, 300] loss: 0.387
[16, 360] loss: 0.388
Epoch: 16 -> Loss: 0.542204737663
Epoch: 16 -> Test Accuracy: 84.4
[17, 60] loss: 0.370
[17, 120] loss: 0.357
[17, 180] loss: 0.377
[17, 240] loss: 0.364
[17, 300] loss: 0.391
[17, 360] loss: 0.408
Epoch: 17 -> Loss: 0.406774371862
Epoch: 17 -> Test Accuracy: 83.69
[18, 60] loss: 0.361
[18, 120] loss: 0.370
[18, 180] loss: 0.374
[18, 240] loss: 0.381
[18, 300] loss: 0.382
[18, 360] loss: 0.378
Epoch: 18 -> Loss: 0.390609174967
Epoch: 18 -> Test Accuracy: 83.99
[19, 60] loss: 0.355
[19, 120] loss: 0.379
[19, 180] loss: 0.360
[19, 240] loss: 0.388
[19, 300] loss: 0.384
[19, 360] loss: 0.398
Epoch: 19 -> Loss: 0.531518101692
Epoch: 19 -> Test Accuracy: 84.16
[20, 60] loss: 0.353
[20, 120] loss: 0.364
[20, 180] loss: 0.369
[20, 240] loss: 0.381
[20, 300] loss: 0.390
[20, 360] loss: 0.372
Epoch: 20 -> Loss: 0.658749222755
Epoch: 20 -> Test Accuracy: 84.1
[21, 60] loss: 0.335
[21, 120] loss: 0.305
[21, 180] loss: 0.287
[21, 240] loss: 0.294
[21, 300] loss: 0.300
[21, 360] loss: 0.288
Epoch: 21 -> Loss: 0.237031057477
Epoch: 21 -> Test Accuracy: 86.04
[22, 60] loss: 0.256
[22, 120] loss: 0.286
[22, 180] loss: 0.250
[22, 240] loss: 0.273
[22, 300] loss: 0.269
[22, 360] loss: 0.267
Epoch: 22 -> Loss: 0.301119506359
Epoch: 22 -> Test Accuracy: 86.37
[23, 60] loss: 0.253
[23, 120] loss: 0.236
[23, 180] loss: 0.257
[23, 240] loss: 0.247
[23, 300] loss: 0.248
[23, 360] loss: 0.244
Epoch: 23 -> Loss: 0.214704036713
Epoch: 23 -> Test Accuracy: 86.59
[24, 60] loss: 0.240
[24, 120] loss: 0.239
[24, 180] loss: 0.237
[24, 240] loss: 0.246
[24, 300] loss: 0.238
[24, 360] loss: 0.242
Epoch: 24 -> Loss: 0.138725206256
Epoch: 24 -> Test Accuracy: 86.59
[25, 60] loss: 0.225
[25, 120] loss: 0.220
[25, 180] loss: 0.215
[25, 240] loss: 0.240
[25, 300] loss: 0.232
[25, 360] loss: 0.227
Epoch: 25 -> Loss: 0.226364284754
Epoch: 25 -> Test Accuracy: 86.27
[26, 60] loss: 0.204
[26, 120] loss: 0.211
[26, 180] loss: 0.230
[26, 240] loss: 0.226
[26, 300] loss: 0.216
[26, 360] loss: 0.224
Epoch: 26 -> Loss: 0.19846278429
Epoch: 26 -> Test Accuracy: 86.16
[27, 60] loss: 0.205
[27, 120] loss: 0.211
[27, 180] loss: 0.219
[27, 240] loss: 0.222
[27, 300] loss: 0.206
[27, 360] loss: 0.225
Epoch: 27 -> Loss: 0.245066836476
Epoch: 27 -> Test Accuracy: 86.59
[28, 60] loss: 0.205
[28, 120] loss: 0.201
[28, 180] loss: 0.215
[28, 240] loss: 0.210
[28, 300] loss: 0.225
[28, 360] loss: 0.207
Epoch: 28 -> Loss: 0.256059736013
Epoch: 28 -> Test Accuracy: 86.18
[29, 60] loss: 0.202
[29, 120] loss: 0.202
[29, 180] loss: 0.225
[29, 240] loss: 0.209
[29, 300] loss: 0.218
[29, 360] loss: 0.212
Epoch: 29 -> Loss: 0.21028470993
Epoch: 29 -> Test Accuracy: 86.18
[30, 60] loss: 0.201
[30, 120] loss: 0.205
[30, 180] loss: 0.201
[30, 240] loss: 0.205
[30, 300] loss: 0.205
[30, 360] loss: 0.215
Epoch: 30 -> Loss: 0.233492895961
Epoch: 30 -> Test Accuracy: 86.2
[31, 60] loss: 0.199
[31, 120] loss: 0.184
[31, 180] loss: 0.197
[31, 240] loss: 0.209
[31, 300] loss: 0.214
[31, 360] loss: 0.212
Epoch: 31 -> Loss: 0.157852530479
Epoch: 31 -> Test Accuracy: 85.71
[32, 60] loss: 0.200
[32, 120] loss: 0.211
[32, 180] loss: 0.197
[32, 240] loss: 0.196
[32, 300] loss: 0.223
[32, 360] loss: 0.212
Epoch: 32 -> Loss: 0.228305101395
Epoch: 32 -> Test Accuracy: 85.88
[33, 60] loss: 0.187
[33, 120] loss: 0.195
[33, 180] loss: 0.205
[33, 240] loss: 0.201
[33, 300] loss: 0.221
[33, 360] loss: 0.207
Epoch: 33 -> Loss: 0.162550657988
Epoch: 33 -> Test Accuracy: 85.6
[34, 60] loss: 0.185
[34, 120] loss: 0.194
[34, 180] loss: 0.190
[34, 240] loss: 0.203
[34, 300] loss: 0.216
[34, 360] loss: 0.214
Epoch: 34 -> Loss: 0.145446151495
Epoch: 34 -> Test Accuracy: 85.77
[35, 60] loss: 0.193
[35, 120] loss: 0.201
[35, 180] loss: 0.202
[35, 240] loss: 0.213
[35, 300] loss: 0.217
[35, 360] loss: 0.197
Epoch: 35 -> Loss: 0.267737209797
Epoch: 35 -> Test Accuracy: 86.16
[36, 60] loss: 0.197
[36, 120] loss: 0.200
[36, 180] loss: 0.198
[36, 240] loss: 0.203
[36, 300] loss: 0.201
[36, 360] loss: 0.209
Epoch: 36 -> Loss: 0.198297768831
Epoch: 36 -> Test Accuracy: 86.07
[37, 60] loss: 0.190
[37, 120] loss: 0.185
[37, 180] loss: 0.209
[37, 240] loss: 0.201
[37, 300] loss: 0.212
[37, 360] loss: 0.214
Epoch: 37 -> Loss: 0.162070557475
Epoch: 37 -> Test Accuracy: 85.59
[38, 60] loss: 0.193
[38, 120] loss: 0.194
[38, 180] loss: 0.203
[38, 240] loss: 0.193
[38, 300] loss: 0.198
[38, 360] loss: 0.213
Epoch: 38 -> Loss: 0.171396017075
Epoch: 38 -> Test Accuracy: 85.38
[39, 60] loss: 0.197
[39, 120] loss: 0.198
[39, 180] loss: 0.205
[39, 240] loss: 0.196
[39, 300] loss: 0.210
[39, 360] loss: 0.198
Epoch: 39 -> Loss: 0.184170261025
Epoch: 39 -> Test Accuracy: 85.57
[40, 60] loss: 0.184
[40, 120] loss: 0.182
[40, 180] loss: 0.185
[40, 240] loss: 0.199
[40, 300] loss: 0.207
[40, 360] loss: 0.203
Epoch: 40 -> Loss: 0.0648645684123
Epoch: 40 -> Test Accuracy: 85.76
[41, 60] loss: 0.178
[41, 120] loss: 0.179
[41, 180] loss: 0.173
[41, 240] loss: 0.157
[41, 300] loss: 0.156
[41, 360] loss: 0.154
Epoch: 41 -> Loss: 0.128126725554
Epoch: 41 -> Test Accuracy: 86.48
[42, 60] loss: 0.147
[42, 120] loss: 0.149
[42, 180] loss: 0.142
[42, 240] loss: 0.143
[42, 300] loss: 0.147
[42, 360] loss: 0.151
Epoch: 42 -> Loss: 0.118626393378
Epoch: 42 -> Test Accuracy: 86.84
[43, 60] loss: 0.137
[43, 120] loss: 0.145
[43, 180] loss: 0.141
[43, 240] loss: 0.136
[43, 300] loss: 0.142
[43, 360] loss: 0.135
Epoch: 43 -> Loss: 0.197865873575
Epoch: 43 -> Test Accuracy: 86.87
[44, 60] loss: 0.120
[44, 120] loss: 0.134
[44, 180] loss: 0.133
[44, 240] loss: 0.123
[44, 300] loss: 0.128
[44, 360] loss: 0.127
Epoch: 44 -> Loss: 0.156142085791
Epoch: 44 -> Test Accuracy: 86.55
[45, 60] loss: 0.121
[45, 120] loss: 0.127
[45, 180] loss: 0.136
[45, 240] loss: 0.128
[45, 300] loss: 0.134
[45, 360] loss: 0.129
Epoch: 45 -> Loss: 0.120640911162
Epoch: 45 -> Test Accuracy: 87.09
[46, 60] loss: 0.119
[46, 120] loss: 0.123
[46, 180] loss: 0.116
[46, 240] loss: 0.110
[46, 300] loss: 0.120
[46, 360] loss: 0.118
Epoch: 46 -> Loss: 0.119994021952
Epoch: 46 -> Test Accuracy: 87.12
[47, 60] loss: 0.118
[47, 120] loss: 0.116
[47, 180] loss: 0.115
[47, 240] loss: 0.126
[47, 300] loss: 0.119
[47, 360] loss: 0.115
Epoch: 47 -> Loss: 0.0461328849196
Epoch: 47 -> Test Accuracy: 87.06
[48, 60] loss: 0.119
[48, 120] loss: 0.119
[48, 180] loss: 0.120
[48, 240] loss: 0.111
[48, 300] loss: 0.115
[48, 360] loss: 0.121
Epoch: 48 -> Loss: 0.0995544865727
Epoch: 48 -> Test Accuracy: 87.16
[49, 60] loss: 0.119
[49, 120] loss: 0.114
[49, 180] loss: 0.110
[49, 240] loss: 0.118
[49, 300] loss: 0.116
[49, 360] loss: 0.107
Epoch: 49 -> Loss: 0.18508708477
Epoch: 49 -> Test Accuracy: 87.23
[50, 60] loss: 0.113
[50, 120] loss: 0.106
[50, 180] loss: 0.112
[50, 240] loss: 0.109
[50, 300] loss: 0.121
[50, 360] loss: 0.115
Epoch: 50 -> Loss: 0.225960090756
Epoch: 50 -> Test Accuracy: 87.3
[51, 60] loss: 0.111
[51, 120] loss: 0.113
[51, 180] loss: 0.113
[51, 240] loss: 0.111
[51, 300] loss: 0.109
[51, 360] loss: 0.104
Epoch: 51 -> Loss: 0.134554818273
Epoch: 51 -> Test Accuracy: 87.08
[52, 60] loss: 0.112
[52, 120] loss: 0.107
[52, 180] loss: 0.104
[52, 240] loss: 0.111
[52, 300] loss: 0.097
[52, 360] loss: 0.120
Epoch: 52 -> Loss: 0.11552156508
Epoch: 52 -> Test Accuracy: 87.08
[53, 60] loss: 0.104
[53, 120] loss: 0.115
[53, 180] loss: 0.110
[53, 240] loss: 0.112
[53, 300] loss: 0.101
[53, 360] loss: 0.106
Epoch: 53 -> Loss: 0.136014476418
Epoch: 53 -> Test Accuracy: 87.19
[54, 60] loss: 0.109
[54, 120] loss: 0.104
[54, 180] loss: 0.102
[54, 240] loss: 0.107
[54, 300] loss: 0.108
[54, 360] loss: 0.108
Epoch: 54 -> Loss: 0.127010077238
Epoch: 54 -> Test Accuracy: 87.15
[55, 60] loss: 0.106
[55, 120] loss: 0.095
[55, 180] loss: 0.107
[55, 240] loss: 0.107
[55, 300] loss: 0.104
[55, 360] loss: 0.106
Epoch: 55 -> Loss: 0.148679152131
Epoch: 55 -> Test Accuracy: 87.08
[56, 60] loss: 0.103
[56, 120] loss: 0.102
[56, 180] loss: 0.103
[56, 240] loss: 0.109
[56, 300] loss: 0.102
[56, 360] loss: 0.107
Epoch: 56 -> Loss: 0.116108573973
Epoch: 56 -> Test Accuracy: 87.21
[57, 60] loss: 0.109
[57, 120] loss: 0.105
[57, 180] loss: 0.103
[57, 240] loss: 0.103
[57, 300] loss: 0.112
[57, 360] loss: 0.107
Epoch: 57 -> Loss: 0.121085688472
Epoch: 57 -> Test Accuracy: 87.12
[58, 60] loss: 0.102
[58, 120] loss: 0.099
[58, 180] loss: 0.104
[58, 240] loss: 0.108
[58, 300] loss: 0.104
[58, 360] loss: 0.104
Epoch: 58 -> Loss: 0.150178059936
Epoch: 58 -> Test Accuracy: 87.28
[59, 60] loss: 0.099
[59, 120] loss: 0.098
[59, 180] loss: 0.101
[59, 240] loss: 0.110
[59, 300] loss: 0.110
[59, 360] loss: 0.106
Epoch: 59 -> Loss: 0.053861014545
Epoch: 59 -> Test Accuracy: 87.17
[60, 60] loss: 0.102
[60, 120] loss: 0.093
[60, 180] loss: 0.097
[60, 240] loss: 0.097
[60, 300] loss: 0.099
[60, 360] loss: 0.105
Epoch: 60 -> Loss: 0.18052020669
Epoch: 60 -> Test Accuracy: 87.17
[61, 60] loss: 0.103
[61, 120] loss: 0.098
[61, 180] loss: 0.097
[61, 240] loss: 0.101
[61, 300] loss: 0.098
[61, 360] loss: 0.105
Epoch: 61 -> Loss: 0.114300295711
Epoch: 61 -> Test Accuracy: 87.17
[62, 60] loss: 0.097
[62, 120] loss: 0.103
[62, 180] loss: 0.102
[62, 240] loss: 0.097
[62, 300] loss: 0.101
[62, 360] loss: 0.100
Epoch: 62 -> Loss: 0.0899142846465
Epoch: 62 -> Test Accuracy: 86.98
[63, 60] loss: 0.102
[63, 120] loss: 0.108
[63, 180] loss: 0.094
[63, 240] loss: 0.104
[63, 300] loss: 0.098
[63, 360] loss: 0.103
Epoch: 63 -> Loss: 0.134980231524
Epoch: 63 -> Test Accuracy: 87.08
[64, 60] loss: 0.103
[64, 120] loss: 0.099
[64, 180] loss: 0.097
[64, 240] loss: 0.097
[64, 300] loss: 0.097
[64, 360] loss: 0.096
Epoch: 64 -> Loss: 0.0731904357672
Epoch: 64 -> Test Accuracy: 87.04
[65, 60] loss: 0.093
[65, 120] loss: 0.104
[65, 180] loss: 0.094
[65, 240] loss: 0.098
[65, 300] loss: 0.091
[65, 360] loss: 0.101
Epoch: 65 -> Loss: 0.134622067213
Epoch: 65 -> Test Accuracy: 87.15
[66, 60] loss: 0.095
[66, 120] loss: 0.091
[66, 180] loss: 0.094
[66, 240] loss: 0.100
[66, 300] loss: 0.092
[66, 360] loss: 0.099
Epoch: 66 -> Loss: 0.0725847557187
Epoch: 66 -> Test Accuracy: 87.05
[67, 60] loss: 0.098
[67, 120] loss: 0.101
[67, 180] loss: 0.096
[67, 240] loss: 0.093
[67, 300] loss: 0.095
[67, 360] loss: 0.095
Epoch: 67 -> Loss: 0.119690179825
Epoch: 67 -> Test Accuracy: 87.04
[68, 60] loss: 0.095
[68, 120] loss: 0.090
[68, 180] loss: 0.097
[68, 240] loss: 0.096
[68, 300] loss: 0.097
[68, 360] loss: 0.098
Epoch: 68 -> Loss: 0.229573771358
Epoch: 68 -> Test Accuracy: 87.05
[69, 60] loss: 0.091
[69, 120] loss: 0.102
[69, 180] loss: 0.096
[69, 240] loss: 0.090
[69, 300] loss: 0.089
[69, 360] loss: 0.099
Epoch: 69 -> Loss: 0.0802088752389
Epoch: 69 -> Test Accuracy: 87.03
[70, 60] loss: 0.099
[70, 120] loss: 0.098
[70, 180] loss: 0.087
[70, 240] loss: 0.087
[70, 300] loss: 0.094
[70, 360] loss: 0.098
Epoch: 70 -> Loss: 0.0723464339972
Epoch: 70 -> Test Accuracy: 87.06
[71, 60] loss: 0.090
[71, 120] loss: 0.098
[71, 180] loss: 0.091
[71, 240] loss: 0.097
[71, 300] loss: 0.098
[71, 360] loss: 0.092
Epoch: 71 -> Loss: 0.0418816134334
Epoch: 71 -> Test Accuracy: 87.12
[72, 60] loss: 0.091
[72, 120] loss: 0.097
[72, 180] loss: 0.088
[72, 240] loss: 0.088
[72, 300] loss: 0.083
[72, 360] loss: 0.096
Epoch: 72 -> Loss: 0.161606714129
Epoch: 72 -> Test Accuracy: 86.95
[73, 60] loss: 0.092
[73, 120] loss: 0.090
[73, 180] loss: 0.093
[73, 240] loss: 0.098
[73, 300] loss: 0.094
[73, 360] loss: 0.088
Epoch: 73 -> Loss: 0.0995782464743
Epoch: 73 -> Test Accuracy: 87.03
[74, 60] loss: 0.088
[74, 120] loss: 0.096
[74, 180] loss: 0.094
[74, 240] loss: 0.091
[74, 300] loss: 0.092
[74, 360] loss: 0.092
Epoch: 74 -> Loss: 0.104454539716
Epoch: 74 -> Test Accuracy: 87.18
[75, 60] loss: 0.096
[75, 120] loss: 0.094
[75, 180] loss: 0.087
[75, 240] loss: 0.084
[75, 300] loss: 0.094
[75, 360] loss: 0.090
Epoch: 75 -> Loss: 0.103685036302
Epoch: 75 -> Test Accuracy: 87.02
[76, 60] loss: 0.088
[76, 120] loss: 0.083
[76, 180] loss: 0.091
[76, 240] loss: 0.090
[76, 300] loss: 0.089
[76, 360] loss: 0.086
Epoch: 76 -> Loss: 0.171830669045
Epoch: 76 -> Test Accuracy: 87.19
[77, 60] loss: 0.087
[77, 120] loss: 0.089
[77, 180] loss: 0.092
[77, 240] loss: 0.089
[77, 300] loss: 0.092
[77, 360] loss: 0.090
Epoch: 77 -> Loss: 0.0278023779392
Epoch: 77 -> Test Accuracy: 87.04
[78, 60] loss: 0.090
[78, 120] loss: 0.084
[78, 180] loss: 0.085
[78, 240] loss: 0.091
[78, 300] loss: 0.101
[78, 360] loss: 0.090
Epoch: 78 -> Loss: 0.163371950388
Epoch: 78 -> Test Accuracy: 87.17
[79, 60] loss: 0.089
[79, 120] loss: 0.095
[79, 180] loss: 0.090
[79, 240] loss: 0.086
[79, 300] loss: 0.093
[79, 360] loss: 0.089
Epoch: 79 -> Loss: 0.116952084005
Epoch: 79 -> Test Accuracy: 87.15
[80, 60] loss: 0.089
[80, 120] loss: 0.091
[80, 180] loss: 0.087
[80, 240] loss: 0.088
[80, 300] loss: 0.092
[80, 360] loss: 0.088
Epoch: 80 -> Loss: 0.177693337202
Epoch: 80 -> Test Accuracy: 87.0
[81, 60] loss: 0.085
[81, 120] loss: 0.092
[81, 180] loss: 0.085
[81, 240] loss: 0.081
[81, 300] loss: 0.092
[81, 360] loss: 0.083
Epoch: 81 -> Loss: 0.0867458954453
Epoch: 81 -> Test Accuracy: 86.99
[82, 60] loss: 0.090
[82, 120] loss: 0.097
[82, 180] loss: 0.082
[82, 240] loss: 0.090
[82, 300] loss: 0.089
[82, 360] loss: 0.088
Epoch: 82 -> Loss: 0.0585666783154
Epoch: 82 -> Test Accuracy: 87.0
[83, 60] loss: 0.084
[83, 120] loss: 0.083
[83, 180] loss: 0.093
[83, 240] loss: 0.091
[83, 300] loss: 0.086
[83, 360] loss: 0.083
Epoch: 83 -> Loss: 0.0910839065909
Epoch: 83 -> Test Accuracy: 87.12
[84, 60] loss: 0.085
[84, 120] loss: 0.082
[84, 180] loss: 0.083
[84, 240] loss: 0.085
[84, 300] loss: 0.082
[84, 360] loss: 0.075
Epoch: 84 -> Loss: 0.049478083849
Epoch: 84 -> Test Accuracy: 86.98
[85, 60] loss: 0.087
[85, 120] loss: 0.084
[85, 180] loss: 0.079
[85, 240] loss: 0.083
[85, 300] loss: 0.079
[85, 360] loss: 0.089
Epoch: 85 -> Loss: 0.0535939820111
Epoch: 85 -> Test Accuracy: 86.85
[86, 60] loss: 0.085
[86, 120] loss: 0.081
[86, 180] loss: 0.081
[86, 240] loss: 0.088
[86, 300] loss: 0.080
[86, 360] loss: 0.078
Epoch: 86 -> Loss: 0.0802417322993
Epoch: 86 -> Test Accuracy: 86.97
[87, 60] loss: 0.081
[87, 120] loss: 0.085
[87, 180] loss: 0.081
[87, 240] loss: 0.083
[87, 300] loss: 0.082
[87, 360] loss: 0.080
Epoch: 87 -> Loss: 0.237124204636
Epoch: 87 -> Test Accuracy: 86.95
[88, 60] loss: 0.082
[88, 120] loss: 0.081
[88, 180] loss: 0.082
[88, 240] loss: 0.081
[88, 300] loss: 0.080
[88, 360] loss: 0.085
Epoch: 88 -> Loss: 0.0808196440339
Epoch: 88 -> Test Accuracy: 86.99
[89, 60] loss: 0.082
[89, 120] loss: 0.080
[89, 180] loss: 0.088
[89, 240] loss: 0.081
[89, 300] loss: 0.083
[89, 360] loss: 0.080
Epoch: 89 -> Loss: 0.0974667519331
Epoch: 89 -> Test Accuracy: 86.9
[90, 60] loss: 0.084
[90, 120] loss: 0.080
[90, 180] loss: 0.080
[90, 240] loss: 0.084
[90, 300] loss: 0.084
[90, 360] loss: 0.081
Epoch: 90 -> Loss: 0.139950841665
Epoch: 90 -> Test Accuracy: 87.1
[91, 60] loss: 0.077
[91, 120] loss: 0.076
[91, 180] loss: 0.081
[91, 240] loss: 0.083
[91, 300] loss: 0.086
[91, 360] loss: 0.083
Epoch: 91 -> Loss: 0.102676652372
Epoch: 91 -> Test Accuracy: 86.91
[92, 60] loss: 0.084
[92, 120] loss: 0.075
[92, 180] loss: 0.080
[92, 240] loss: 0.081
[92, 300] loss: 0.085
[92, 360] loss: 0.083
Epoch: 92 -> Loss: 0.184652641416
Epoch: 92 -> Test Accuracy: 87.05
[93, 60] loss: 0.085
[93, 120] loss: 0.078
[93, 180] loss: 0.078
[93, 240] loss: 0.076
[93, 300] loss: 0.086
[93, 360] loss: 0.086
Epoch: 93 -> Loss: 0.121937416494
Epoch: 93 -> Test Accuracy: 87.01
[94, 60] loss: 0.087
[94, 120] loss: 0.077
[94, 180] loss: 0.082
[94, 240] loss: 0.084
[94, 300] loss: 0.078
[94, 360] loss: 0.086
Epoch: 94 -> Loss: 0.0756267905235
Epoch: 94 -> Test Accuracy: 86.91
[95, 60] loss: 0.081
[95, 120] loss: 0.073
[95, 180] loss: 0.075
[95, 240] loss: 0.086
[95, 300] loss: 0.083
[95, 360] loss: 0.080
Epoch: 95 -> Loss: 0.0927665904164
Epoch: 95 -> Test Accuracy: 87.07
[96, 60] loss: 0.076
[96, 120] loss: 0.075
[96, 180] loss: 0.079
[96, 240] loss: 0.077
[96, 300] loss: 0.085
[96, 360] loss: 0.084
Epoch: 96 -> Loss: 0.0530608296394
Epoch: 96 -> Test Accuracy: 86.98
[97, 60] loss: 0.082
[97, 120] loss: 0.083
[97, 180] loss: 0.086
[97, 240] loss: 0.074
[97, 300] loss: 0.079
[97, 360] loss: 0.075
Epoch: 97 -> Loss: 0.116631627083
Epoch: 97 -> Test Accuracy: 86.9
[98, 60] loss: 0.078
[98, 120] loss: 0.072
[98, 180] loss: 0.080
[98, 240] loss: 0.080
[98, 300] loss: 0.071
[98, 360] loss: 0.079
Epoch: 98 -> Loss: 0.0896067619324
Epoch: 98 -> Test Accuracy: 86.88
[99, 60] loss: 0.073
[99, 120] loss: 0.077
[99, 180] loss: 0.078
[99, 240] loss: 0.079
[99, 300] loss: 0.076
[99, 360] loss: 0.079
Epoch: 99 -> Loss: 0.0433508194983
Epoch: 99 -> Test Accuracy: 86.93
[100, 60] loss: 0.074
[100, 120] loss: 0.076
[100, 180] loss: 0.074
[100, 240] loss: 0.076
[100, 300] loss: 0.066
[100, 360] loss: 0.078
Epoch: 100 -> Loss: 0.0526957735419
Epoch: 100 -> Test Accuracy: 86.98
Finished Training
[1, 60] loss: 1.605
[1, 120] loss: 0.846
[1, 180] loss: 0.789
[1, 240] loss: 0.733
[1, 300] loss: 0.681
[1, 360] loss: 0.677
Epoch: 1 -> Loss: 0.769748389721
Epoch: 1 -> Test Accuracy: 74.84
[2, 60] loss: 0.651
[2, 120] loss: 0.633
[2, 180] loss: 0.632
[2, 240] loss: 0.607
[2, 300] loss: 0.605
[2, 360] loss: 0.616
Epoch: 2 -> Loss: 0.742862284184
Epoch: 2 -> Test Accuracy: 77.06
[3, 60] loss: 0.568
[3, 120] loss: 0.549
[3, 180] loss: 0.576
[3, 240] loss: 0.564
[3, 300] loss: 0.575
[3, 360] loss: 0.577
Epoch: 3 -> Loss: 0.471496880054
Epoch: 3 -> Test Accuracy: 79.05
[4, 60] loss: 0.523
[4, 120] loss: 0.529
[4, 180] loss: 0.523
[4, 240] loss: 0.545
[4, 300] loss: 0.549
[4, 360] loss: 0.539
Epoch: 4 -> Loss: 0.706794142723
Epoch: 4 -> Test Accuracy: 79.15
[5, 60] loss: 0.503
[5, 120] loss: 0.496
[5, 180] loss: 0.533
[5, 240] loss: 0.522
[5, 300] loss: 0.519
[5, 360] loss: 0.508
Epoch: 5 -> Loss: 0.490466743708
Epoch: 5 -> Test Accuracy: 79.89
[6, 60] loss: 0.473
[6, 120] loss: 0.499
[6, 180] loss: 0.504
[6, 240] loss: 0.515
[6, 300] loss: 0.498
[6, 360] loss: 0.493
Epoch: 6 -> Loss: 0.545969605446
Epoch: 6 -> Test Accuracy: 79.9
[7, 60] loss: 0.492
[7, 120] loss: 0.469
[7, 180] loss: 0.488
[7, 240] loss: 0.483
[7, 300] loss: 0.517
[7, 360] loss: 0.486
Epoch: 7 -> Loss: 0.551766335964
Epoch: 7 -> Test Accuracy: 80.26
[8, 60] loss: 0.477
[8, 120] loss: 0.478
[8, 180] loss: 0.496
[8, 240] loss: 0.461
[8, 300] loss: 0.504
[8, 360] loss: 0.487
Epoch: 8 -> Loss: 0.427379071712
Epoch: 8 -> Test Accuracy: 80.36
[9, 60] loss: 0.472
[9, 120] loss: 0.462
[9, 180] loss: 0.465
[9, 240] loss: 0.477
[9, 300] loss: 0.477
[9, 360] loss: 0.478
Epoch: 9 -> Loss: 0.429799467325
Epoch: 9 -> Test Accuracy: 79.96
[10, 60] loss: 0.454
[10, 120] loss: 0.465
[10, 180] loss: 0.463
[10, 240] loss: 0.471
[10, 300] loss: 0.463
[10, 360] loss: 0.464
Epoch: 10 -> Loss: 0.401413530111
Epoch: 10 -> Test Accuracy: 80.69
[11, 60] loss: 0.444
[11, 120] loss: 0.448
[11, 180] loss: 0.444
[11, 240] loss: 0.481
[11, 300] loss: 0.482
[11, 360] loss: 0.477
Epoch: 11 -> Loss: 0.395341336727
Epoch: 11 -> Test Accuracy: 80.08
[12, 60] loss: 0.434
[12, 120] loss: 0.448
[12, 180] loss: 0.454
[12, 240] loss: 0.459
[12, 300] loss: 0.473
[12, 360] loss: 0.464
Epoch: 12 -> Loss: 0.510496497154
Epoch: 12 -> Test Accuracy: 80.42
[13, 60] loss: 0.430
[13, 120] loss: 0.435
[13, 180] loss: 0.457
[13, 240] loss: 0.459
[13, 300] loss: 0.465
[13, 360] loss: 0.451
Epoch: 13 -> Loss: 0.47343057394
Epoch: 13 -> Test Accuracy: 80.07
[14, 60] loss: 0.434
[14, 120] loss: 0.442
[14, 180] loss: 0.437
[14, 240] loss: 0.448
[14, 300] loss: 0.445
[14, 360] loss: 0.454
Epoch: 14 -> Loss: 0.414171218872
Epoch: 14 -> Test Accuracy: 80.18
[15, 60] loss: 0.432
[15, 120] loss: 0.448
[15, 180] loss: 0.452
[15, 240] loss: 0.448
[15, 300] loss: 0.451
[15, 360] loss: 0.453
Epoch: 15 -> Loss: 0.561669826508
Epoch: 15 -> Test Accuracy: 80.31
[16, 60] loss: 0.438
[16, 120] loss: 0.429
[16, 180] loss: 0.430
[16, 240] loss: 0.450
[16, 300] loss: 0.457
[16, 360] loss: 0.454
Epoch: 16 -> Loss: 0.567278921604
Epoch: 16 -> Test Accuracy: 80.55
[17, 60] loss: 0.429
[17, 120] loss: 0.418
[17, 180] loss: 0.448
[17, 240] loss: 0.437
[17, 300] loss: 0.462
[17, 360] loss: 0.465
Epoch: 17 -> Loss: 0.570365846157
Epoch: 17 -> Test Accuracy: 80.12
[18, 60] loss: 0.417
[18, 120] loss: 0.419
[18, 180] loss: 0.441
[18, 240] loss: 0.447
[18, 300] loss: 0.458
[18, 360] loss: 0.457
Epoch: 18 -> Loss: 0.421803236008
Epoch: 18 -> Test Accuracy: 80.08
[19, 60] loss: 0.415
[19, 120] loss: 0.421
[19, 180] loss: 0.433
[19, 240] loss: 0.439
[19, 300] loss: 0.441
[19, 360] loss: 0.444
Epoch: 19 -> Loss: 0.302561104298
Epoch: 19 -> Test Accuracy: 80.31
[20, 60] loss: 0.421
[20, 120] loss: 0.416
[20, 180] loss: 0.429
[20, 240] loss: 0.437
[20, 300] loss: 0.443
[20, 360] loss: 0.471
Epoch: 20 -> Loss: 0.446769177914
Epoch: 20 -> Test Accuracy: 80.8
[21, 60] loss: 0.377
[21, 120] loss: 0.370
[21, 180] loss: 0.358
[21, 240] loss: 0.392
[21, 300] loss: 0.357
[21, 360] loss: 0.363
Epoch: 21 -> Loss: 0.316772520542
Epoch: 21 -> Test Accuracy: 82.42
[22, 60] loss: 0.336
[22, 120] loss: 0.334
[22, 180] loss: 0.342
[22, 240] loss: 0.346
[22, 300] loss: 0.342
[22, 360] loss: 0.336
Epoch: 22 -> Loss: 0.357571542263
Epoch: 22 -> Test Accuracy: 82.4
[23, 60] loss: 0.322
[23, 120] loss: 0.326
[23, 180] loss: 0.321
[23, 240] loss: 0.319
[23, 300] loss: 0.322
[23, 360] loss: 0.328
Epoch: 23 -> Loss: 0.370080560446
Epoch: 23 -> Test Accuracy: 82.57
[24, 60] loss: 0.310
[24, 120] loss: 0.316
[24, 180] loss: 0.314
[24, 240] loss: 0.321
[24, 300] loss: 0.315
[24, 360] loss: 0.321
Epoch: 24 -> Loss: 0.189372211695
Epoch: 24 -> Test Accuracy: 82.79
[25, 60] loss: 0.301
[25, 120] loss: 0.307
[25, 180] loss: 0.297
[25, 240] loss: 0.307
[25, 300] loss: 0.317
[25, 360] loss: 0.303
Epoch: 25 -> Loss: 0.396466910839
Epoch: 25 -> Test Accuracy: 82.75
[26, 60] loss: 0.292
[26, 120] loss: 0.299
[26, 180] loss: 0.301
[26, 240] loss: 0.315
[26, 300] loss: 0.304
[26, 360] loss: 0.302
Epoch: 26 -> Loss: 0.238028690219
Epoch: 26 -> Test Accuracy: 82.81
[27, 60] loss: 0.293
[27, 120] loss: 0.296
[27, 180] loss: 0.285
[27, 240] loss: 0.299
[27, 300] loss: 0.295
[27, 360] loss: 0.288
Epoch: 27 -> Loss: 0.278031021357
Epoch: 27 -> Test Accuracy: 82.71
[28, 60] loss: 0.291
[28, 120] loss: 0.287
[28, 180] loss: 0.283
[28, 240] loss: 0.302
[28, 300] loss: 0.269
[28, 360] loss: 0.292
Epoch: 28 -> Loss: 0.435865014791
Epoch: 28 -> Test Accuracy: 82.66
[29, 60] loss: 0.291
[29, 120] loss: 0.280
[29, 180] loss: 0.283
[29, 240] loss: 0.295
[29, 300] loss: 0.295
[29, 360] loss: 0.291
Epoch: 29 -> Loss: 0.275308996439
Epoch: 29 -> Test Accuracy: 82.79
[30, 60] loss: 0.280
[30, 120] loss: 0.270
[30, 180] loss: 0.277
[30, 240] loss: 0.294
[30, 300] loss: 0.288
[30, 360] loss: 0.301
Epoch: 30 -> Loss: 0.280865430832
Epoch: 30 -> Test Accuracy: 82.21
[31, 60] loss: 0.277
[31, 120] loss: 0.284
[31, 180] loss: 0.283
[31, 240] loss: 0.291
[31, 300] loss: 0.271
[31, 360] loss: 0.297
Epoch: 31 -> Loss: 0.391873121262
Epoch: 31 -> Test Accuracy: 82.69
[32, 60] loss: 0.269
[32, 120] loss: 0.289
[32, 180] loss: 0.264
[32, 240] loss: 0.282
[32, 300] loss: 0.289
[32, 360] loss: 0.291
Epoch: 32 -> Loss: 0.343095004559
Epoch: 32 -> Test Accuracy: 82.28
[33, 60] loss: 0.278
[33, 120] loss: 0.273
[33, 180] loss: 0.273
[33, 240] loss: 0.281
[33, 300] loss: 0.285
[33, 360] loss: 0.280
Epoch: 33 -> Loss: 0.279102951288
Epoch: 33 -> Test Accuracy: 81.93
[34, 60] loss: 0.273
[34, 120] loss: 0.266
[34, 180] loss: 0.292
[34, 240] loss: 0.283
[34, 300] loss: 0.285
[34, 360] loss: 0.272
Epoch: 34 -> Loss: 0.251658469439
Epoch: 34 -> Test Accuracy: 82.35
[35, 60] loss: 0.260
[35, 120] loss: 0.278
[35, 180] loss: 0.274
[35, 240] loss: 0.278
[35, 300] loss: 0.288
[35, 360] loss: 0.299
Epoch: 35 -> Loss: 0.244260117412
Epoch: 35 -> Test Accuracy: 82.33
[36, 60] loss: 0.267
[36, 120] loss: 0.268
[36, 180] loss: 0.270
[36, 240] loss: 0.296
[36, 300] loss: 0.266
[36, 360] loss: 0.285
Epoch: 36 -> Loss: 0.333917081356
Epoch: 36 -> Test Accuracy: 81.92
[37, 60] loss: 0.269
[37, 120] loss: 0.261
[37, 180] loss: 0.284
[37, 240] loss: 0.262
[37, 300] loss: 0.286
[37, 360] loss: 0.282
Epoch: 37 -> Loss: 0.405453115702
Epoch: 37 -> Test Accuracy: 82.28
[38, 60] loss: 0.252
[38, 120] loss: 0.272
[38, 180] loss: 0.263
[38, 240] loss: 0.270
[38, 300] loss: 0.275
[38, 360] loss: 0.273
Epoch: 38 -> Loss: 0.405525028706
Epoch: 38 -> Test Accuracy: 81.91
[39, 60] loss: 0.267
[39, 120] loss: 0.252
[39, 180] loss: 0.268
[39, 240] loss: 0.268
[39, 300] loss: 0.290
[39, 360] loss: 0.297
Epoch: 39 -> Loss: 0.246826142073
Epoch: 39 -> Test Accuracy: 82.42
[40, 60] loss: 0.269
[40, 120] loss: 0.268
[40, 180] loss: 0.261
[40, 240] loss: 0.260
[40, 300] loss: 0.257
[40, 360] loss: 0.284
Epoch: 40 -> Loss: 0.45331415534
Epoch: 40 -> Test Accuracy: 82.42
[41, 60] loss: 0.236
[41, 120] loss: 0.252
[41, 180] loss: 0.250
[41, 240] loss: 0.238
[41, 300] loss: 0.241
[41, 360] loss: 0.232
Epoch: 41 -> Loss: 0.207646131516
Epoch: 41 -> Test Accuracy: 82.86
[42, 60] loss: 0.222
[42, 120] loss: 0.233
[42, 180] loss: 0.216
[42, 240] loss: 0.213
[42, 300] loss: 0.231
[42, 360] loss: 0.223
Epoch: 42 -> Loss: 0.273645073175
Epoch: 42 -> Test Accuracy: 83.3
[43, 60] loss: 0.206
[43, 120] loss: 0.220
[43, 180] loss: 0.208
[43, 240] loss: 0.215
[43, 300] loss: 0.220
[43, 360] loss: 0.209
Epoch: 43 -> Loss: 0.0931783020496
Epoch: 43 -> Test Accuracy: 83.09
[44, 60] loss: 0.207
[44, 120] loss: 0.201
[44, 180] loss: 0.207
[44, 240] loss: 0.214
[44, 300] loss: 0.209
[44, 360] loss: 0.196
Epoch: 44 -> Loss: 0.167782276869
Epoch: 44 -> Test Accuracy: 83.05
[45, 60] loss: 0.202
[45, 120] loss: 0.194
[45, 180] loss: 0.199
[45, 240] loss: 0.191
[45, 300] loss: 0.199
[45, 360] loss: 0.198
Epoch: 45 -> Loss: 0.162827700377
Epoch: 45 -> Test Accuracy: 83.47
[46, 60] loss: 0.189
[46, 120] loss: 0.192
[46, 180] loss: 0.190
[46, 240] loss: 0.191
[46, 300] loss: 0.195
[46, 360] loss: 0.198
Epoch: 46 -> Loss: 0.235953241587
Epoch: 46 -> Test Accuracy: 83.36
[47, 60] loss: 0.194
[47, 120] loss: 0.194
[47, 180] loss: 0.188
[47, 240] loss: 0.182
[47, 300] loss: 0.188
[47, 360] loss: 0.194
Epoch: 47 -> Loss: 0.223013162613
Epoch: 47 -> Test Accuracy: 83.47
[48, 60] loss: 0.180
[48, 120] loss: 0.190
[48, 180] loss: 0.181
[48, 240] loss: 0.187
[48, 300] loss: 0.199
[48, 360] loss: 0.190
Epoch: 48 -> Loss: 0.139108017087
Epoch: 48 -> Test Accuracy: 83.31
[49, 60] loss: 0.190
[49, 120] loss: 0.187
[49, 180] loss: 0.179
[49, 240] loss: 0.183
[49, 300] loss: 0.194
[49, 360] loss: 0.188
Epoch: 49 -> Loss: 0.14915433526
Epoch: 49 -> Test Accuracy: 83.36
[50, 60] loss: 0.183
[50, 120] loss: 0.182
[50, 180] loss: 0.194
[50, 240] loss: 0.190
[50, 300] loss: 0.186
[50, 360] loss: 0.180
Epoch: 50 -> Loss: 0.106967367232
Epoch: 50 -> Test Accuracy: 83.36
[51, 60] loss: 0.186
[51, 120] loss: 0.175
[51, 180] loss: 0.182
[51, 240] loss: 0.186
[51, 300] loss: 0.181
[51, 360] loss: 0.183
Epoch: 51 -> Loss: 0.187744662166
Epoch: 51 -> Test Accuracy: 83.28
[52, 60] loss: 0.177
[52, 120] loss: 0.176
[52, 180] loss: 0.184
[52, 240] loss: 0.188
[52, 300] loss: 0.186
[52, 360] loss: 0.174
Epoch: 52 -> Loss: 0.201156467199
Epoch: 52 -> Test Accuracy: 83.39
[53, 60] loss: 0.173
[53, 120] loss: 0.184
[53, 180] loss: 0.180
[53, 240] loss: 0.188
[53, 300] loss: 0.188
[53, 360] loss: 0.172
Epoch: 53 -> Loss: 0.0869134441018
Epoch: 53 -> Test Accuracy: 83.35
[54, 60] loss: 0.180
[54, 120] loss: 0.173
[54, 180] loss: 0.182
[54, 240] loss: 0.170
[54, 300] loss: 0.177
[54, 360] loss: 0.168
Epoch: 54 -> Loss: 0.182309672236
Epoch: 54 -> Test Accuracy: 83.37
[55, 60] loss: 0.175
[55, 120] loss: 0.175
[55, 180] loss: 0.178
[55, 240] loss: 0.197
[55, 300] loss: 0.170
[55, 360] loss: 0.174
Epoch: 55 -> Loss: 0.215438812971
Epoch: 55 -> Test Accuracy: 83.33
[56, 60] loss: 0.178
[56, 120] loss: 0.180
[56, 180] loss: 0.178
[56, 240] loss: 0.172
[56, 300] loss: 0.181
[56, 360] loss: 0.165
Epoch: 56 -> Loss: 0.229026034474
Epoch: 56 -> Test Accuracy: 83.44
[57, 60] loss: 0.173
[57, 120] loss: 0.175
[57, 180] loss: 0.180
[57, 240] loss: 0.171
[57, 300] loss: 0.178
[57, 360] loss: 0.176
Epoch: 57 -> Loss: 0.236210376024
Epoch: 57 -> Test Accuracy: 83.46
[58, 60] loss: 0.173
[58, 120] loss: 0.177
[58, 180] loss: 0.173
[58, 240] loss: 0.177
[58, 300] loss: 0.175
[58, 360] loss: 0.181
Epoch: 58 -> Loss: 0.195679098368
Epoch: 58 -> Test Accuracy: 83.43
[59, 60] loss: 0.171
[59, 120] loss: 0.172
[59, 180] loss: 0.176
[59, 240] loss: 0.173
[59, 300] loss: 0.169
[59, 360] loss: 0.167
Epoch: 59 -> Loss: 0.11266014725
Epoch: 59 -> Test Accuracy: 83.51
[60, 60] loss: 0.170
[60, 120] loss: 0.180
[60, 180] loss: 0.162
[60, 240] loss: 0.170
[60, 300] loss: 0.166
[60, 360] loss: 0.177
Epoch: 60 -> Loss: 0.191300824285
Epoch: 60 -> Test Accuracy: 83.55
[61, 60] loss: 0.162
[61, 120] loss: 0.162
[61, 180] loss: 0.174
[61, 240] loss: 0.174
[61, 300] loss: 0.175
[61, 360] loss: 0.171
Epoch: 61 -> Loss: 0.285082429647
Epoch: 61 -> Test Accuracy: 83.53
[62, 60] loss: 0.166
[62, 120] loss: 0.179
[62, 180] loss: 0.171
[62, 240] loss: 0.156
[62, 300] loss: 0.167
[62, 360] loss: 0.165
Epoch: 62 -> Loss: 0.17471639812
Epoch: 62 -> Test Accuracy: 83.68
[63, 60] loss: 0.166
[63, 120] loss: 0.169
[63, 180] loss: 0.168
[63, 240] loss: 0.164
[63, 300] loss: 0.163
[63, 360] loss: 0.159
Epoch: 63 -> Loss: 0.27798050642
Epoch: 63 -> Test Accuracy: 83.62
[64, 60] loss: 0.172
[64, 120] loss: 0.171
[64, 180] loss: 0.169
[64, 240] loss: 0.171
[64, 300] loss: 0.173
[64, 360] loss: 0.166
Epoch: 64 -> Loss: 0.182154223323
Epoch: 64 -> Test Accuracy: 83.73
[65, 60] loss: 0.178
[65, 120] loss: 0.171
[65, 180] loss: 0.164
[65, 240] loss: 0.161
[65, 300] loss: 0.167
[65, 360] loss: 0.165
Epoch: 65 -> Loss: 0.278418779373
Epoch: 65 -> Test Accuracy: 83.6
[66, 60] loss: 0.165
[66, 120] loss: 0.161
[66, 180] loss: 0.160
[66, 240] loss: 0.173
[66, 300] loss: 0.170
[66, 360] loss: 0.158
Epoch: 66 -> Loss: 0.153344780207
Epoch: 66 -> Test Accuracy: 83.56
[67, 60] loss: 0.157
[67, 120] loss: 0.159
[67, 180] loss: 0.173
[67, 240] loss: 0.160
[67, 300] loss: 0.176
[67, 360] loss: 0.171
Epoch: 67 -> Loss: 0.167253404856
Epoch: 67 -> Test Accuracy: 83.56
[68, 60] loss: 0.171
[68, 120] loss: 0.164
[68, 180] loss: 0.157
[68, 240] loss: 0.164
[68, 300] loss: 0.161
[68, 360] loss: 0.170
Epoch: 68 -> Loss: 0.137328147888
Epoch: 68 -> Test Accuracy: 83.62
[69, 60] loss: 0.156
[69, 120] loss: 0.153
[69, 180] loss: 0.172
[69, 240] loss: 0.165
[69, 300] loss: 0.155
[69, 360] loss: 0.162
Epoch: 69 -> Loss: 0.244012355804
Epoch: 69 -> Test Accuracy: 83.62
[70, 60] loss: 0.168
[70, 120] loss: 0.172
[70, 180] loss: 0.155
[70, 240] loss: 0.156
[70, 300] loss: 0.169
[70, 360] loss: 0.165
Epoch: 70 -> Loss: 0.161557644606
Epoch: 70 -> Test Accuracy: 83.72
[71, 60] loss: 0.166
[71, 120] loss: 0.160
[71, 180] loss: 0.154
[71, 240] loss: 0.169
[71, 300] loss: 0.159
[71, 360] loss: 0.169
Epoch: 71 -> Loss: 0.236004680395
Epoch: 71 -> Test Accuracy: 83.72
[72, 60] loss: 0.166
[72, 120] loss: 0.156
[72, 180] loss: 0.164
[72, 240] loss: 0.157
[72, 300] loss: 0.164
[72, 360] loss: 0.155
Epoch: 72 -> Loss: 0.163643166423
Epoch: 72 -> Test Accuracy: 83.76
[73, 60] loss: 0.152
[73, 120] loss: 0.157
[73, 180] loss: 0.155
[73, 240] loss: 0.161
[73, 300] loss: 0.160
[73, 360] loss: 0.156
Epoch: 73 -> Loss: 0.225978657603
Epoch: 73 -> Test Accuracy: 83.64
[74, 60] loss: 0.158
[74, 120] loss: 0.167
[74, 180] loss: 0.163
[74, 240] loss: 0.155
[74, 300] loss: 0.159
[74, 360] loss: 0.151
Epoch: 74 -> Loss: 0.10319314152
Epoch: 74 -> Test Accuracy: 83.79
[75, 60] loss: 0.163
[75, 120] loss: 0.158
[75, 180] loss: 0.159
[75, 240] loss: 0.148
[75, 300] loss: 0.153
[75, 360] loss: 0.159
Epoch: 75 -> Loss: 0.128170013428
Epoch: 75 -> Test Accuracy: 83.71
[76, 60] loss: 0.154
[76, 120] loss: 0.149
[76, 180] loss: 0.151
[76, 240] loss: 0.157
[76, 300] loss: 0.155
[76, 360] loss: 0.157
Epoch: 76 -> Loss: 0.192204624414
Epoch: 76 -> Test Accuracy: 83.75
[77, 60] loss: 0.168
[77, 120] loss: 0.157
[77, 180] loss: 0.156
[77, 240] loss: 0.151
[77, 300] loss: 0.160
[77, 360] loss: 0.163
Epoch: 77 -> Loss: 0.104353502393
Epoch: 77 -> Test Accuracy: 83.66
[78, 60] loss: 0.154
[78, 120] loss: 0.154
[78, 180] loss: 0.160
[78, 240] loss: 0.164
[78, 300] loss: 0.156
[78, 360] loss: 0.156
Epoch: 78 -> Loss: 0.0883823335171
Epoch: 78 -> Test Accuracy: 83.64
[79, 60] loss: 0.154
[79, 120] loss: 0.155
[79, 180] loss: 0.155
[79, 240] loss: 0.144
[79, 300] loss: 0.158
[79, 360] loss: 0.156
Epoch: 79 -> Loss: 0.155757188797
Epoch: 79 -> Test Accuracy: 83.64
[80, 60] loss: 0.149
[80, 120] loss: 0.151
[80, 180] loss: 0.151
[80, 240] loss: 0.163
[80, 300] loss: 0.151
[80, 360] loss: 0.151
Epoch: 80 -> Loss: 0.0908910185099
Epoch: 80 -> Test Accuracy: 83.64
[81, 60] loss: 0.158
[81, 120] loss: 0.152
[81, 180] loss: 0.155
[81, 240] loss: 0.154
[81, 300] loss: 0.149
[81, 360] loss: 0.154
Epoch: 81 -> Loss: 0.234690546989
Epoch: 81 -> Test Accuracy: 83.57
[82, 60] loss: 0.155
[82, 120] loss: 0.152
[82, 180] loss: 0.151
[82, 240] loss: 0.151
[82, 300] loss: 0.149
[82, 360] loss: 0.147
Epoch: 82 -> Loss: 0.235760167241
Epoch: 82 -> Test Accuracy: 83.59
[83, 60] loss: 0.153
[83, 120] loss: 0.151
[83, 180] loss: 0.155
[83, 240] loss: 0.156
[83, 300] loss: 0.152
[83, 360] loss: 0.149
Epoch: 83 -> Loss: 0.20064611733
Epoch: 83 -> Test Accuracy: 83.6
[84, 60] loss: 0.146
[84, 120] loss: 0.147
[84, 180] loss: 0.145
[84, 240] loss: 0.154
[84, 300] loss: 0.146
[84, 360] loss: 0.154
Epoch: 84 -> Loss: 0.202218964696
Epoch: 84 -> Test Accuracy: 83.55
[85, 60] loss: 0.151
[85, 120] loss: 0.155
[85, 180] loss: 0.156
[85, 240] loss: 0.141
[85, 300] loss: 0.150
[85, 360] loss: 0.149
Epoch: 85 -> Loss: 0.15076392889
Epoch: 85 -> Test Accuracy: 83.65
[86, 60] loss: 0.151
[86, 120] loss: 0.152
[86, 180] loss: 0.146
[86, 240] loss: 0.149
[86, 300] loss: 0.146
[86, 360] loss: 0.151
Epoch: 86 -> Loss: 0.150267452002
Epoch: 86 -> Test Accuracy: 83.52
[87, 60] loss: 0.145
[87, 120] loss: 0.149
[87, 180] loss: 0.149
[87, 240] loss: 0.139
[87, 300] loss: 0.147
[87, 360] loss: 0.150
Epoch: 87 -> Loss: 0.21535487473
Epoch: 87 -> Test Accuracy: 83.51
[88, 60] loss: 0.142
[88, 120] loss: 0.142
[88, 180] loss: 0.147
[88, 240] loss: 0.149
[88, 300] loss: 0.152
[88, 360] loss: 0.149
Epoch: 88 -> Loss: 0.199831798673
Epoch: 88 -> Test Accuracy: 83.53
[89, 60] loss: 0.141
[89, 120] loss: 0.142
[89, 180] loss: 0.140
[89, 240] loss: 0.144
[89, 300] loss: 0.150
[89, 360] loss: 0.154
Epoch: 89 -> Loss: 0.176730006933
Epoch: 89 -> Test Accuracy: 83.55
[90, 60] loss: 0.140
[90, 120] loss: 0.139
[90, 180] loss: 0.148
[90, 240] loss: 0.148
[90, 300] loss: 0.162
[90, 360] loss: 0.153
Epoch: 90 -> Loss: 0.120919801295
Epoch: 90 -> Test Accuracy: 83.51
[91, 60] loss: 0.146
[91, 120] loss: 0.149
[91, 180] loss: 0.143
[91, 240] loss: 0.147
[91, 300] loss: 0.150
[91, 360] loss: 0.142
Epoch: 91 -> Loss: 0.200098872185
Epoch: 91 -> Test Accuracy: 83.45
[92, 60] loss: 0.141
[92, 120] loss: 0.148
[92, 180] loss: 0.150
[92, 240] loss: 0.140
[92, 300] loss: 0.150
[92, 360] loss: 0.152
Epoch: 92 -> Loss: 0.272257626057
Epoch: 92 -> Test Accuracy: 83.67
[93, 60] loss: 0.134
[93, 120] loss: 0.146
[93, 180] loss: 0.135
[93, 240] loss: 0.156
[93, 300] loss: 0.136
[93, 360] loss: 0.141
Epoch: 93 -> Loss: 0.132149830461
Epoch: 93 -> Test Accuracy: 83.68
[94, 60] loss: 0.146
[94, 120] loss: 0.148
[94, 180] loss: 0.139
[94, 240] loss: 0.146
[94, 300] loss: 0.149
[94, 360] loss: 0.145
Epoch: 94 -> Loss: 0.156441152096
Epoch: 94 -> Test Accuracy: 83.57
[95, 60] loss: 0.136
[95, 120] loss: 0.147
[95, 180] loss: 0.145
[95, 240] loss: 0.138
[95, 300] loss: 0.146
[95, 360] loss: 0.145
Epoch: 95 -> Loss: 0.163300901651
Epoch: 95 -> Test Accuracy: 83.71
[96, 60] loss: 0.144
[96, 120] loss: 0.138
[96, 180] loss: 0.139
[96, 240] loss: 0.147
[96, 300] loss: 0.141
[96, 360] loss: 0.150
Epoch: 96 -> Loss: 0.138925388455
Epoch: 96 -> Test Accuracy: 83.62
[97, 60] loss: 0.133
[97, 120] loss: 0.148
[97, 180] loss: 0.142
[97, 240] loss: 0.141
[97, 300] loss: 0.143
[97, 360] loss: 0.144
Epoch: 97 -> Loss: 0.102161839604
Epoch: 97 -> Test Accuracy: 83.65
[98, 60] loss: 0.140
[98, 120] loss: 0.137
[98, 180] loss: 0.129
[98, 240] loss: 0.139
[98, 300] loss: 0.139
[98, 360] loss: 0.146
Epoch: 98 -> Loss: 0.102180123329
Epoch: 98 -> Test Accuracy: 83.63
[99, 60] loss: 0.140
[99, 120] loss: 0.147
[99, 180] loss: 0.137
[99, 240] loss: 0.128
[99, 300] loss: 0.141
[99, 360] loss: 0.144
Epoch: 99 -> Loss: 0.225455522537
Epoch: 99 -> Test Accuracy: 83.59
[100, 60] loss: 0.144
[100, 120] loss: 0.142
[100, 180] loss: 0.141
[100, 240] loss: 0.136
[100, 300] loss: 0.138
[100, 360] loss: 0.134
Epoch: 100 -> Loss: 0.0870344862342
Epoch: 100 -> Test Accuracy: 83.74
Finished Training
[1, 60] loss: 1.992
[1, 120] loss: 1.202
[1, 180] loss: 1.107
[1, 240] loss: 1.072
[1, 300] loss: 0.994
[1, 360] loss: 1.001
Epoch: 1 -> Loss: 0.888210117817
Epoch: 1 -> Test Accuracy: 60.67
[2, 60] loss: 0.973
[2, 120] loss: 0.949
[2, 180] loss: 0.926
[2, 240] loss: 0.932
[2, 300] loss: 0.921
[2, 360] loss: 0.890
Epoch: 2 -> Loss: 0.802006244659
Epoch: 2 -> Test Accuracy: 63.53
[3, 60] loss: 0.880
[3, 120] loss: 0.873
[3, 180] loss: 0.894
[3, 240] loss: 0.856
[3, 300] loss: 0.872
[3, 360] loss: 0.861
Epoch: 3 -> Loss: 0.733943283558
Epoch: 3 -> Test Accuracy: 65.67
[4, 60] loss: 0.836
[4, 120] loss: 0.848
[4, 180] loss: 0.838
[4, 240] loss: 0.841
[4, 300] loss: 0.839
[4, 360] loss: 0.840
Epoch: 4 -> Loss: 0.755034208298
Epoch: 4 -> Test Accuracy: 66.71
[5, 60] loss: 0.832
[5, 120] loss: 0.817
[5, 180] loss: 0.815
[5, 240] loss: 0.818
[5, 300] loss: 0.834
[5, 360] loss: 0.807
Epoch: 5 -> Loss: 0.732703328133
Epoch: 5 -> Test Accuracy: 66.19
[6, 60] loss: 0.810
[6, 120] loss: 0.808
[6, 180] loss: 0.836
[6, 240] loss: 0.827
[6, 300] loss: 0.813
[6, 360] loss: 0.792
Epoch: 6 -> Loss: 0.864481449127
Epoch: 6 -> Test Accuracy: 67.37
[7, 60] loss: 0.809
[7, 120] loss: 0.803
[7, 180] loss: 0.805
[7, 240] loss: 0.806
[7, 300] loss: 0.797
[7, 360] loss: 0.794
Epoch: 7 -> Loss: 1.01008820534
Epoch: 7 -> Test Accuracy: 67.29
[8, 60] loss: 0.791
[8, 120] loss: 0.785
[8, 180] loss: 0.792
[8, 240] loss: 0.802
[8, 300] loss: 0.803
[8, 360] loss: 0.793
Epoch: 8 -> Loss: 0.807754516602
Epoch: 8 -> Test Accuracy: 67.06
[9, 60] loss: 0.787
[9, 120] loss: 0.795
[9, 180] loss: 0.793
[9, 240] loss: 0.779
[9, 300] loss: 0.779
[9, 360] loss: 0.787
Epoch: 9 -> Loss: 0.865053653717
Epoch: 9 -> Test Accuracy: 67.92
[10, 60] loss: 0.798
[10, 120] loss: 0.774
[10, 180] loss: 0.772
[10, 240] loss: 0.778
[10, 300] loss: 0.797
[10, 360] loss: 0.789
Epoch: 10 -> Loss: 1.00264394283
Epoch: 10 -> Test Accuracy: 67.31
[11, 60] loss: 0.765
[11, 120] loss: 0.788
[11, 180] loss: 0.776
[11, 240] loss: 0.780
[11, 300] loss: 0.789
[11, 360] loss: 0.786
Epoch: 11 -> Loss: 0.86000585556
Epoch: 11 -> Test Accuracy: 67.72
[12, 60] loss: 0.747
[12, 120] loss: 0.782
[12, 180] loss: 0.759
[12, 240] loss: 0.779
[12, 300] loss: 0.804
[12, 360] loss: 0.799
Epoch: 12 -> Loss: 0.719414174557
Epoch: 12 -> Test Accuracy: 68.17
[13, 60] loss: 0.775
[13, 120] loss: 0.765
[13, 180] loss: 0.778
[13, 240] loss: 0.779
[13, 300] loss: 0.783
[13, 360] loss: 0.771
Epoch: 13 -> Loss: 0.556922793388
Epoch: 13 -> Test Accuracy: 67.7
[14, 60] loss: 0.767
[14, 120] loss: 0.781
[14, 180] loss: 0.786
[14, 240] loss: 0.771
[14, 300] loss: 0.760
[14, 360] loss: 0.791
Epoch: 14 -> Loss: 0.718975305557
Epoch: 14 -> Test Accuracy: 68.25
[15, 60] loss: 0.766
[15, 120] loss: 0.771
[15, 180] loss: 0.760
[15, 240] loss: 0.768
[15, 300] loss: 0.761
[15, 360] loss: 0.762
Epoch: 15 -> Loss: 1.00635278225
Epoch: 15 -> Test Accuracy: 68.01
[16, 60] loss: 0.752
[16, 120] loss: 0.779
[16, 180] loss: 0.773
[16, 240] loss: 0.779
[16, 300] loss: 0.761
[16, 360] loss: 0.764
Epoch: 16 -> Loss: 0.615387439728
Epoch: 16 -> Test Accuracy: 68.28
[17, 60] loss: 0.756
[17, 120] loss: 0.766
[17, 180] loss: 0.762
[17, 240] loss: 0.770
[17, 300] loss: 0.766
[17, 360] loss: 0.778
Epoch: 17 -> Loss: 0.857590973377
Epoch: 17 -> Test Accuracy: 67.68
[18, 60] loss: 0.747
[18, 120] loss: 0.748
[18, 180] loss: 0.773
[18, 240] loss: 0.784
[18, 300] loss: 0.758
[18, 360] loss: 0.759
Epoch: 18 -> Loss: 0.73320287466
Epoch: 18 -> Test Accuracy: 68.2
[19, 60] loss: 0.761
[19, 120] loss: 0.769
[19, 180] loss: 0.747
[19, 240] loss: 0.765
[19, 300] loss: 0.754
[19, 360] loss: 0.760
Epoch: 19 -> Loss: 0.831901073456
Epoch: 19 -> Test Accuracy: 68.74
[20, 60] loss: 0.757
[20, 120] loss: 0.756
[20, 180] loss: 0.772
[20, 240] loss: 0.747
[20, 300] loss: 0.762
[20, 360] loss: 0.758
Epoch: 20 -> Loss: 0.672998905182
Epoch: 20 -> Test Accuracy: 68.56
[21, 60] loss: 0.716
[21, 120] loss: 0.697
[21, 180] loss: 0.697
[21, 240] loss: 0.668
[21, 300] loss: 0.656
[21, 360] loss: 0.666
Epoch: 21 -> Loss: 0.708767354488
Epoch: 21 -> Test Accuracy: 70.52
[22, 60] loss: 0.682
[22, 120] loss: 0.660
[22, 180] loss: 0.658
[22, 240] loss: 0.655
[22, 300] loss: 0.644
[22, 360] loss: 0.642
Epoch: 22 -> Loss: 0.594831764698
Epoch: 22 -> Test Accuracy: 71.68
[23, 60] loss: 0.632
[23, 120] loss: 0.630
[23, 180] loss: 0.635
[23, 240] loss: 0.637
[23, 300] loss: 0.631
[23, 360] loss: 0.652
Epoch: 23 -> Loss: 0.603423058987
Epoch: 23 -> Test Accuracy: 71.55
[24, 60] loss: 0.632
[24, 120] loss: 0.620
[24, 180] loss: 0.627
[24, 240] loss: 0.623
[24, 300] loss: 0.630
[24, 360] loss: 0.639
Epoch: 24 -> Loss: 0.603372693062
Epoch: 24 -> Test Accuracy: 71.9
[25, 60] loss: 0.623
[25, 120] loss: 0.628
[25, 180] loss: 0.639
[25, 240] loss: 0.601
[25, 300] loss: 0.632
[25, 360] loss: 0.626
Epoch: 25 -> Loss: 0.616600453854
Epoch: 25 -> Test Accuracy: 71.63
[26, 60] loss: 0.609
[26, 120] loss: 0.640
[26, 180] loss: 0.600
[26, 240] loss: 0.610
[26, 300] loss: 0.592
[26, 360] loss: 0.637
Epoch: 26 -> Loss: 0.608256340027
Epoch: 26 -> Test Accuracy: 72.46
[27, 60] loss: 0.608
[27, 120] loss: 0.624
[27, 180] loss: 0.601
[27, 240] loss: 0.627
[27, 300] loss: 0.604
[27, 360] loss: 0.643
Epoch: 27 -> Loss: 0.691409289837
Epoch: 27 -> Test Accuracy: 71.53
[28, 60] loss: 0.615
[28, 120] loss: 0.607
[28, 180] loss: 0.612
[28, 240] loss: 0.630
[28, 300] loss: 0.616
[28, 360] loss: 0.615
Epoch: 28 -> Loss: 0.613882958889
Epoch: 28 -> Test Accuracy: 72.31
[29, 60] loss: 0.611
[29, 120] loss: 0.606
[29, 180] loss: 0.625
[29, 240] loss: 0.623
[29, 300] loss: 0.606
[29, 360] loss: 0.616
Epoch: 29 -> Loss: 0.467459022999
Epoch: 29 -> Test Accuracy: 72.25
[30, 60] loss: 0.610
[30, 120] loss: 0.625
[30, 180] loss: 0.606
[30, 240] loss: 0.618
[30, 300] loss: 0.610
[30, 360] loss: 0.614
Epoch: 30 -> Loss: 0.833746314049
Epoch: 30 -> Test Accuracy: 72.45
[31, 60] loss: 0.607
[31, 120] loss: 0.608
[31, 180] loss: 0.630
[31, 240] loss: 0.606
[31, 300] loss: 0.614
[31, 360] loss: 0.596
Epoch: 31 -> Loss: 0.659976422787
Epoch: 31 -> Test Accuracy: 71.82
[32, 60] loss: 0.614
[32, 120] loss: 0.597
[32, 180] loss: 0.616
[32, 240] loss: 0.601
[32, 300] loss: 0.607
[32, 360] loss: 0.623
Epoch: 32 -> Loss: 0.54079246521
Epoch: 32 -> Test Accuracy: 71.52
[33, 60] loss: 0.603
[33, 120] loss: 0.626
[33, 180] loss: 0.604
[33, 240] loss: 0.611
[33, 300] loss: 0.583
[33, 360] loss: 0.614
Epoch: 33 -> Loss: 0.495990037918
Epoch: 33 -> Test Accuracy: 71.8
[34, 60] loss: 0.600
[34, 120] loss: 0.601
[34, 180] loss: 0.596
[34, 240] loss: 0.602
[34, 300] loss: 0.620
[34, 360] loss: 0.600
Epoch: 34 -> Loss: 0.66494768858
Epoch: 34 -> Test Accuracy: 71.88
[35, 60] loss: 0.595
[35, 120] loss: 0.617
[35, 180] loss: 0.625
[35, 240] loss: 0.607
[35, 300] loss: 0.590
[35, 360] loss: 0.622
Epoch: 35 -> Loss: 0.629398822784
Epoch: 35 -> Test Accuracy: 72.17
[36, 60] loss: 0.609
[36, 120] loss: 0.584
[36, 180] loss: 0.606
[36, 240] loss: 0.605
[36, 300] loss: 0.616
[36, 360] loss: 0.607
Epoch: 36 -> Loss: 0.558319091797
Epoch: 36 -> Test Accuracy: 72.36
[37, 60] loss: 0.599
[37, 120] loss: 0.606
[37, 180] loss: 0.601
[37, 240] loss: 0.600
[37, 300] loss: 0.603
[37, 360] loss: 0.612
Epoch: 37 -> Loss: 0.560134530067
Epoch: 37 -> Test Accuracy: 72.43
[38, 60] loss: 0.590
[38, 120] loss: 0.613
[38, 180] loss: 0.617
[38, 240] loss: 0.618
[38, 300] loss: 0.603
[38, 360] loss: 0.603
Epoch: 38 -> Loss: 0.708665013313
Epoch: 38 -> Test Accuracy: 72.11
[39, 60] loss: 0.603
[39, 120] loss: 0.614
[39, 180] loss: 0.598
[39, 240] loss: 0.612
[39, 300] loss: 0.615
[39, 360] loss: 0.606
Epoch: 39 -> Loss: 0.652741909027
Epoch: 39 -> Test Accuracy: 72.6
[40, 60] loss: 0.580
[40, 120] loss: 0.595
[40, 180] loss: 0.591
[40, 240] loss: 0.612
[40, 300] loss: 0.604
[40, 360] loss: 0.622
Epoch: 40 -> Loss: 0.494285404682
Epoch: 40 -> Test Accuracy: 71.79
[41, 60] loss: 0.571
[41, 120] loss: 0.574
[41, 180] loss: 0.549
[41, 240] loss: 0.574
[41, 300] loss: 0.553
[41, 360] loss: 0.548
Epoch: 41 -> Loss: 0.537647306919
Epoch: 41 -> Test Accuracy: 73.24
[42, 60] loss: 0.551
[42, 120] loss: 0.557
[42, 180] loss: 0.538
[42, 240] loss: 0.547
[42, 300] loss: 0.523
[42, 360] loss: 0.536
Epoch: 42 -> Loss: 0.584192574024
Epoch: 42 -> Test Accuracy: 73.78
[43, 60] loss: 0.537
[43, 120] loss: 0.525
[43, 180] loss: 0.539
[43, 240] loss: 0.546
[43, 300] loss: 0.525
[43, 360] loss: 0.535
Epoch: 43 -> Loss: 0.75417226553
Epoch: 43 -> Test Accuracy: 73.71
[44, 60] loss: 0.526
[44, 120] loss: 0.518
[44, 180] loss: 0.523
[44, 240] loss: 0.527
[44, 300] loss: 0.512
[44, 360] loss: 0.518
Epoch: 44 -> Loss: 0.419095039368
Epoch: 44 -> Test Accuracy: 74.0
[45, 60] loss: 0.522
[45, 120] loss: 0.509
[45, 180] loss: 0.497
[45, 240] loss: 0.524
[45, 300] loss: 0.527
[45, 360] loss: 0.521
Epoch: 45 -> Loss: 0.390105068684
Epoch: 45 -> Test Accuracy: 74.02
[46, 60] loss: 0.506
[46, 120] loss: 0.499
[46, 180] loss: 0.513
[46, 240] loss: 0.515
[46, 300] loss: 0.514
[46, 360] loss: 0.508
Epoch: 46 -> Loss: 0.426923751831
Epoch: 46 -> Test Accuracy: 74.18
[47, 60] loss: 0.535
[47, 120] loss: 0.511
[47, 180] loss: 0.503
[47, 240] loss: 0.537
[47, 300] loss: 0.500
[47, 360] loss: 0.499
Epoch: 47 -> Loss: 0.380633890629
Epoch: 47 -> Test Accuracy: 74.3
[48, 60] loss: 0.519
[48, 120] loss: 0.508
[48, 180] loss: 0.500
[48, 240] loss: 0.499
[48, 300] loss: 0.483
[48, 360] loss: 0.486
Epoch: 48 -> Loss: 0.623250901699
Epoch: 48 -> Test Accuracy: 74.36
[49, 60] loss: 0.497
[49, 120] loss: 0.492
[49, 180] loss: 0.505
[49, 240] loss: 0.502
[49, 300] loss: 0.508
[49, 360] loss: 0.506
Epoch: 49 -> Loss: 0.539638578892
Epoch: 49 -> Test Accuracy: 74.28
[50, 60] loss: 0.494
[50, 120] loss: 0.513
[50, 180] loss: 0.495
[50, 240] loss: 0.500
[50, 300] loss: 0.507
[50, 360] loss: 0.500
Epoch: 50 -> Loss: 0.487371295691
Epoch: 50 -> Test Accuracy: 74.31
[51, 60] loss: 0.493
[51, 120] loss: 0.494
[51, 180] loss: 0.491
[51, 240] loss: 0.507
[51, 300] loss: 0.498
[51, 360] loss: 0.503
Epoch: 51 -> Loss: 0.542852222919
Epoch: 51 -> Test Accuracy: 74.13
[52, 60] loss: 0.485
[52, 120] loss: 0.497
[52, 180] loss: 0.504
[52, 240] loss: 0.504
[52, 300] loss: 0.498
[52, 360] loss: 0.481
Epoch: 52 -> Loss: 0.670035123825
Epoch: 52 -> Test Accuracy: 74.38
[53, 60] loss: 0.499
[53, 120] loss: 0.497
[53, 180] loss: 0.485
[53, 240] loss: 0.502
[53, 300] loss: 0.504
[53, 360] loss: 0.507
Epoch: 53 -> Loss: 0.512040674686
Epoch: 53 -> Test Accuracy: 74.27
[54, 60] loss: 0.492
[54, 120] loss: 0.498
[54, 180] loss: 0.477
[54, 240] loss: 0.486
[54, 300] loss: 0.483
[54, 360] loss: 0.491
Epoch: 54 -> Loss: 0.403091520071
Epoch: 54 -> Test Accuracy: 74.45
[55, 60] loss: 0.494
[55, 120] loss: 0.498
[55, 180] loss: 0.493
[55, 240] loss: 0.507
[55, 300] loss: 0.497
[55, 360] loss: 0.496
Epoch: 55 -> Loss: 0.333195716143
Epoch: 55 -> Test Accuracy: 74.37
[56, 60] loss: 0.488
[56, 120] loss: 0.479
[56, 180] loss: 0.512
[56, 240] loss: 0.490
[56, 300] loss: 0.492
[56, 360] loss: 0.492
Epoch: 56 -> Loss: 0.65333122015
Epoch: 56 -> Test Accuracy: 74.53
[57, 60] loss: 0.484
[57, 120] loss: 0.497
[57, 180] loss: 0.489
[57, 240] loss: 0.481
[57, 300] loss: 0.483
[57, 360] loss: 0.495
Epoch: 57 -> Loss: 0.746136724949
Epoch: 57 -> Test Accuracy: 74.45
[58, 60] loss: 0.492
[58, 120] loss: 0.505
[58, 180] loss: 0.515
[58, 240] loss: 0.496
[58, 300] loss: 0.484
[58, 360] loss: 0.482
Epoch: 58 -> Loss: 0.403785765171
Epoch: 58 -> Test Accuracy: 74.54
[59, 60] loss: 0.481
[59, 120] loss: 0.491
[59, 180] loss: 0.490
[59, 240] loss: 0.484
[59, 300] loss: 0.484
[59, 360] loss: 0.488
Epoch: 59 -> Loss: 0.374584436417
Epoch: 59 -> Test Accuracy: 74.79
[60, 60] loss: 0.481
[60, 120] loss: 0.487
[60, 180] loss: 0.483
[60, 240] loss: 0.501
[60, 300] loss: 0.494
[60, 360] loss: 0.495
Epoch: 60 -> Loss: 0.36842250824
Epoch: 60 -> Test Accuracy: 74.52
[61, 60] loss: 0.490
[61, 120] loss: 0.479
[61, 180] loss: 0.492
[61, 240] loss: 0.480
[61, 300] loss: 0.478
[61, 360] loss: 0.481
Epoch: 61 -> Loss: 0.533181369305
Epoch: 61 -> Test Accuracy: 74.47
[62, 60] loss: 0.502
[62, 120] loss: 0.470
[62, 180] loss: 0.501
[62, 240] loss: 0.480
[62, 300] loss: 0.473
[62, 360] loss: 0.469
Epoch: 62 -> Loss: 0.488505065441
Epoch: 62 -> Test Accuracy: 74.44
[63, 60] loss: 0.489
[63, 120] loss: 0.482
[63, 180] loss: 0.486
[63, 240] loss: 0.479
[63, 300] loss: 0.492
[63, 360] loss: 0.496
Epoch: 63 -> Loss: 0.505281805992
Epoch: 63 -> Test Accuracy: 74.81
[64, 60] loss: 0.480
[64, 120] loss: 0.490
[64, 180] loss: 0.479
[64, 240] loss: 0.482
[64, 300] loss: 0.487
[64, 360] loss: 0.496
Epoch: 64 -> Loss: 0.606616079807
Epoch: 64 -> Test Accuracy: 74.7
[65, 60] loss: 0.500
[65, 120] loss: 0.480
[65, 180] loss: 0.470
[65, 240] loss: 0.479
[65, 300] loss: 0.487
[65, 360] loss: 0.482
Epoch: 65 -> Loss: 0.629588782787
Epoch: 65 -> Test Accuracy: 74.61
[66, 60] loss: 0.485
[66, 120] loss: 0.495
[66, 180] loss: 0.478
[66, 240] loss: 0.487
[66, 300] loss: 0.469
[66, 360] loss: 0.474
Epoch: 66 -> Loss: 0.429427206516
Epoch: 66 -> Test Accuracy: 74.81
[67, 60] loss: 0.468
[67, 120] loss: 0.478
[67, 180] loss: 0.495
[67, 240] loss: 0.491
[67, 300] loss: 0.486
[67, 360] loss: 0.476
Epoch: 67 -> Loss: 0.418888032436
Epoch: 67 -> Test Accuracy: 74.74
[68, 60] loss: 0.463
[68, 120] loss: 0.465
[68, 180] loss: 0.494
[68, 240] loss: 0.484
[68, 300] loss: 0.475
[68, 360] loss: 0.511
Epoch: 68 -> Loss: 0.612156569958
Epoch: 68 -> Test Accuracy: 74.77
[69, 60] loss: 0.478
[69, 120] loss: 0.483
[69, 180] loss: 0.483
[69, 240] loss: 0.482
[69, 300] loss: 0.476
[69, 360] loss: 0.468
Epoch: 69 -> Loss: 0.6005885005
Epoch: 69 -> Test Accuracy: 74.73
[70, 60] loss: 0.464
[70, 120] loss: 0.493
[70, 180] loss: 0.470
[70, 240] loss: 0.478
[70, 300] loss: 0.482
[70, 360] loss: 0.479
Epoch: 70 -> Loss: 0.673352837563
Epoch: 70 -> Test Accuracy: 74.79
[71, 60] loss: 0.485
[71, 120] loss: 0.491
[71, 180] loss: 0.478
[71, 240] loss: 0.480
[71, 300] loss: 0.457
[71, 360] loss: 0.476
Epoch: 71 -> Loss: 0.438875764608
Epoch: 71 -> Test Accuracy: 74.59
[72, 60] loss: 0.487
[72, 120] loss: 0.471
[72, 180] loss: 0.476
[72, 240] loss: 0.456
[72, 300] loss: 0.474
[72, 360] loss: 0.488
Epoch: 72 -> Loss: 0.525793015957
Epoch: 72 -> Test Accuracy: 74.68
[73, 60] loss: 0.465
[73, 120] loss: 0.482
[73, 180] loss: 0.466
[73, 240] loss: 0.487
[73, 300] loss: 0.474
[73, 360] loss: 0.505
Epoch: 73 -> Loss: 0.405533373356
Epoch: 73 -> Test Accuracy: 74.89
[74, 60] loss: 0.474
[74, 120] loss: 0.475
[74, 180] loss: 0.464
[74, 240] loss: 0.488
[74, 300] loss: 0.484
[74, 360] loss: 0.474
Epoch: 74 -> Loss: 0.626014411449
Epoch: 74 -> Test Accuracy: 74.86
[75, 60] loss: 0.481
[75, 120] loss: 0.479
[75, 180] loss: 0.477
[75, 240] loss: 0.473
[75, 300] loss: 0.472
[75, 360] loss: 0.467
Epoch: 75 -> Loss: 0.482714742422
Epoch: 75 -> Test Accuracy: 74.84
[76, 60] loss: 0.481
[76, 120] loss: 0.450
[76, 180] loss: 0.480
[76, 240] loss: 0.480
[76, 300] loss: 0.465
[76, 360] loss: 0.469
Epoch: 76 -> Loss: 0.434392929077
Epoch: 76 -> Test Accuracy: 74.81
[77, 60] loss: 0.458
[77, 120] loss: 0.473
[77, 180] loss: 0.478
[77, 240] loss: 0.457
[77, 300] loss: 0.487
[77, 360] loss: 0.489
Epoch: 77 -> Loss: 0.608023047447
Epoch: 77 -> Test Accuracy: 74.88
[78, 60] loss: 0.473
[78, 120] loss: 0.476
[78, 180] loss: 0.468
[78, 240] loss: 0.469
[78, 300] loss: 0.460
[78, 360] loss: 0.487
Epoch: 78 -> Loss: 0.561837613583
Epoch: 78 -> Test Accuracy: 74.88
[79, 60] loss: 0.482
[79, 120] loss: 0.479
[79, 180] loss: 0.464
[79, 240] loss: 0.475
[79, 300] loss: 0.472
[79, 360] loss: 0.481
Epoch: 79 -> Loss: 0.610006928444
Epoch: 79 -> Test Accuracy: 74.86
[80, 60] loss: 0.472
[80, 120] loss: 0.470
[80, 180] loss: 0.476
[80, 240] loss: 0.470
[80, 300] loss: 0.481
[80, 360] loss: 0.469
Epoch: 80 -> Loss: 0.624663233757
Epoch: 80 -> Test Accuracy: 74.8
[81, 60] loss: 0.481
[81, 120] loss: 0.466
[81, 180] loss: 0.472
[81, 240] loss: 0.473
[81, 300] loss: 0.463
[81, 360] loss: 0.460
Epoch: 81 -> Loss: 0.552075564861
Epoch: 81 -> Test Accuracy: 74.83
[82, 60] loss: 0.465
[82, 120] loss: 0.464
[82, 180] loss: 0.473
[82, 240] loss: 0.453
[82, 300] loss: 0.473
[82, 360] loss: 0.473
Epoch: 82 -> Loss: 0.435061633587
Epoch: 82 -> Test Accuracy: 74.76
[83, 60] loss: 0.464
[83, 120] loss: 0.475
[83, 180] loss: 0.471
[83, 240] loss: 0.466
[83, 300] loss: 0.478
[83, 360] loss: 0.457
Epoch: 83 -> Loss: 0.518538594246
Epoch: 83 -> Test Accuracy: 74.86
[84, 60] loss: 0.456
[84, 120] loss: 0.467
[84, 180] loss: 0.472
[84, 240] loss: 0.484
[84, 300] loss: 0.481
[84, 360] loss: 0.452
Epoch: 84 -> Loss: 0.371880471706
Epoch: 84 -> Test Accuracy: 74.77
[85, 60] loss: 0.455
[85, 120] loss: 0.456
[85, 180] loss: 0.477
[85, 240] loss: 0.476
[85, 300] loss: 0.468
[85, 360] loss: 0.469
Epoch: 85 -> Loss: 0.559426724911
Epoch: 85 -> Test Accuracy: 74.75
[86, 60] loss: 0.477
[86, 120] loss: 0.465
[86, 180] loss: 0.481
[86, 240] loss: 0.468
[86, 300] loss: 0.468
[86, 360] loss: 0.463
Epoch: 86 -> Loss: 0.523461937904
Epoch: 86 -> Test Accuracy: 74.9
[87, 60] loss: 0.462
[87, 120] loss: 0.458
[87, 180] loss: 0.466
[87, 240] loss: 0.464
[87, 300] loss: 0.476
[87, 360] loss: 0.475
Epoch: 87 -> Loss: 0.357460737228
Epoch: 87 -> Test Accuracy: 75.07
[88, 60] loss: 0.452
[88, 120] loss: 0.470
[88, 180] loss: 0.475
[88, 240] loss: 0.445
[88, 300] loss: 0.478
[88, 360] loss: 0.466
Epoch: 88 -> Loss: 0.404544979334
Epoch: 88 -> Test Accuracy: 75.1
[89, 60] loss: 0.451
[89, 120] loss: 0.458
[89, 180] loss: 0.456
[89, 240] loss: 0.477
[89, 300] loss: 0.466
[89, 360] loss: 0.458
Epoch: 89 -> Loss: 0.439839839935
Epoch: 89 -> Test Accuracy: 74.95
[90, 60] loss: 0.464
[90, 120] loss: 0.452
[90, 180] loss: 0.471
[90, 240] loss: 0.464
[90, 300] loss: 0.457
[90, 360] loss: 0.480
Epoch: 90 -> Loss: 0.471448481083
Epoch: 90 -> Test Accuracy: 74.75
[91, 60] loss: 0.452
[91, 120] loss: 0.468
[91, 180] loss: 0.457
[91, 240] loss: 0.483
[91, 300] loss: 0.457
[91, 360] loss: 0.464
Epoch: 91 -> Loss: 0.596991837025
Epoch: 91 -> Test Accuracy: 74.89
[92, 60] loss: 0.451
[92, 120] loss: 0.462
[92, 180] loss: 0.453
[92, 240] loss: 0.458
[92, 300] loss: 0.472
[92, 360] loss: 0.486
Epoch: 92 -> Loss: 0.657341241837
Epoch: 92 -> Test Accuracy: 74.9
[93, 60] loss: 0.455
[93, 120] loss: 0.452
[93, 180] loss: 0.461
[93, 240] loss: 0.465
[93, 300] loss: 0.442
[93, 360] loss: 0.455
Epoch: 93 -> Loss: 0.535405635834
Epoch: 93 -> Test Accuracy: 74.8
[94, 60] loss: 0.460
[94, 120] loss: 0.466
[94, 180] loss: 0.468
[94, 240] loss: 0.475
[94, 300] loss: 0.450
[94, 360] loss: 0.460
Epoch: 94 -> Loss: 0.549831449986
Epoch: 94 -> Test Accuracy: 74.98
[95, 60] loss: 0.458
[95, 120] loss: 0.462
[95, 180] loss: 0.468
[95, 240] loss: 0.468
[95, 300] loss: 0.461
[95, 360] loss: 0.463
Epoch: 95 -> Loss: 0.603649318218
Epoch: 95 -> Test Accuracy: 75.06
[96, 60] loss: 0.453
[96, 120] loss: 0.454
[96, 180] loss: 0.466
[96, 240] loss: 0.456
[96, 300] loss: 0.453
[96, 360] loss: 0.458
Epoch: 96 -> Loss: 0.593458533287
Epoch: 96 -> Test Accuracy: 74.84
[97, 60] loss: 0.451
[97, 120] loss: 0.475
[97, 180] loss: 0.452
[97, 240] loss: 0.461
[97, 300] loss: 0.458
[97, 360] loss: 0.451
Epoch: 97 -> Loss: 0.493361532688
Epoch: 97 -> Test Accuracy: 75.17
[98, 60] loss: 0.450
[98, 120] loss: 0.456
[98, 180] loss: 0.473
[98, 240] loss: 0.464
[98, 300] loss: 0.466
[98, 360] loss: 0.458
Epoch: 98 -> Loss: 0.489044964314
Epoch: 98 -> Test Accuracy: 74.98
[99, 60] loss: 0.455
[99, 120] loss: 0.455
[99, 180] loss: 0.449
[99, 240] loss: 0.450
[99, 300] loss: 0.458
[99, 360] loss: 0.468
Epoch: 99 -> Loss: 0.518808782101
Epoch: 99 -> Test Accuracy: 74.67
[100, 60] loss: 0.442
[100, 120] loss: 0.463
[100, 180] loss: 0.458
[100, 240] loss: 0.453
[100, 300] loss: 0.453
[100, 360] loss: 0.461
Epoch: 100 -> Loss: 0.443654119968
Epoch: 100 -> Test Accuracy: 75.13
Finished Training
[1, 60] loss: 2.802
[1, 120] loss: 2.132
[1, 180] loss: 2.093
[1, 240] loss: 2.042
[1, 300] loss: 2.024
[1, 360] loss: 1.988
Epoch: 1 -> Loss: 1.84517610073
Epoch: 1 -> Test Accuracy: 26.21
[2, 60] loss: 1.976
[2, 120] loss: 1.960
[2, 180] loss: 1.948
[2, 240] loss: 1.929
[2, 300] loss: 1.928
[2, 360] loss: 1.913
Epoch: 2 -> Loss: 1.92097055912
Epoch: 2 -> Test Accuracy: 27.91
[3, 60] loss: 1.906
[3, 120] loss: 1.906
[3, 180] loss: 1.900
[3, 240] loss: 1.878
[3, 300] loss: 1.884
[3, 360] loss: 1.867
Epoch: 3 -> Loss: 1.91858863831
Epoch: 3 -> Test Accuracy: 28.33
[4, 60] loss: 1.874
[4, 120] loss: 1.874
[4, 180] loss: 1.866
[4, 240] loss: 1.871
[4, 300] loss: 1.846
[4, 360] loss: 1.847
Epoch: 4 -> Loss: 1.81260871887
Epoch: 4 -> Test Accuracy: 30.25
[5, 60] loss: 1.851
[5, 120] loss: 1.842
[5, 180] loss: 1.848
[5, 240] loss: 1.845
[5, 300] loss: 1.848
[5, 360] loss: 1.840
Epoch: 5 -> Loss: 2.05401945114
Epoch: 5 -> Test Accuracy: 30.04
[6, 60] loss: 1.833
[6, 120] loss: 1.826
[6, 180] loss: 1.848
[6, 240] loss: 1.822
[6, 300] loss: 1.825
[6, 360] loss: 1.809
Epoch: 6 -> Loss: 1.8115953207
Epoch: 6 -> Test Accuracy: 30.62
[7, 60] loss: 1.824
[7, 120] loss: 1.817
[7, 180] loss: 1.845
[7, 240] loss: 1.836
[7, 300] loss: 1.814
[7, 360] loss: 1.828
Epoch: 7 -> Loss: 2.0414352417
Epoch: 7 -> Test Accuracy: 31.99
[8, 60] loss: 1.813
[8, 120] loss: 1.830
[8, 180] loss: 1.834
[8, 240] loss: 1.820
[8, 300] loss: 1.821
[8, 360] loss: 1.804
Epoch: 8 -> Loss: 1.76224195957
Epoch: 8 -> Test Accuracy: 31.04
[9, 60] loss: 1.798
[9, 120] loss: 1.803
[9, 180] loss: 1.819
[9, 240] loss: 1.829
[9, 300] loss: 1.822
[9, 360] loss: 1.813
Epoch: 9 -> Loss: 1.81824815273
Epoch: 9 -> Test Accuracy: 31.84
[10, 60] loss: 1.808
[10, 120] loss: 1.823
[10, 180] loss: 1.811
[10, 240] loss: 1.820
[10, 300] loss: 1.803
[10, 360] loss: 1.811
Epoch: 10 -> Loss: 1.829033494
Epoch: 10 -> Test Accuracy: 31.31
[11, 60] loss: 1.784
[11, 120] loss: 1.824
[11, 180] loss: 1.803
[11, 240] loss: 1.802
[11, 300] loss: 1.810
[11, 360] loss: 1.793
Epoch: 11 -> Loss: 1.78773093224
Epoch: 11 -> Test Accuracy: 31.66
[12, 60] loss: 1.809
[12, 120] loss: 1.795
[12, 180] loss: 1.796
[12, 240] loss: 1.796
[12, 300] loss: 1.793
[12, 360] loss: 1.795
Epoch: 12 -> Loss: 1.76458454132
Epoch: 12 -> Test Accuracy: 30.73
[13, 60] loss: 1.822
[13, 120] loss: 1.784
[13, 180] loss: 1.799
[13, 240] loss: 1.794
[13, 300] loss: 1.798
[13, 360] loss: 1.783
Epoch: 13 -> Loss: 1.91414809227
Epoch: 13 -> Test Accuracy: 31.58
[14, 60] loss: 1.793
[14, 120] loss: 1.793
[14, 180] loss: 1.796
[14, 240] loss: 1.792
[14, 300] loss: 1.795
[14, 360] loss: 1.780
Epoch: 14 -> Loss: 1.82072198391
Epoch: 14 -> Test Accuracy: 31.69
[15, 60] loss: 1.797
[15, 120] loss: 1.790
[15, 180] loss: 1.784
[15, 240] loss: 1.796
[15, 300] loss: 1.791
[15, 360] loss: 1.800
Epoch: 15 -> Loss: 1.71568238735
Epoch: 15 -> Test Accuracy: 31.9
[16, 60] loss: 1.802
[16, 120] loss: 1.797
[16, 180] loss: 1.772
[16, 240] loss: 1.789
[16, 300] loss: 1.797
[16, 360] loss: 1.782
Epoch: 16 -> Loss: 1.79029119015
Epoch: 16 -> Test Accuracy: 31.96
[17, 60] loss: 1.788
[17, 120] loss: 1.782
[17, 180] loss: 1.783
[17, 240] loss: 1.805
[17, 300] loss: 1.791
[17, 360] loss: 1.780
Epoch: 17 -> Loss: 1.95210969448
Epoch: 17 -> Test Accuracy: 32.56
[18, 60] loss: 1.796
[18, 120] loss: 1.784
[18, 180] loss: 1.779
[18, 240] loss: 1.807
[18, 300] loss: 1.804
[18, 360] loss: 1.787
Epoch: 18 -> Loss: 1.68113386631
Epoch: 18 -> Test Accuracy: 32.32
[19, 60] loss: 1.782
[19, 120] loss: 1.792
[19, 180] loss: 1.789
[19, 240] loss: 1.785
[19, 300] loss: 1.777
[19, 360] loss: 1.794
Epoch: 19 -> Loss: 1.90905153751
Epoch: 19 -> Test Accuracy: 33.03
[20, 60] loss: 1.771
[20, 120] loss: 1.779
[20, 180] loss: 1.766
[20, 240] loss: 1.790
[20, 300] loss: 1.783
[20, 360] loss: 1.792
Epoch: 20 -> Loss: 1.83599281311
Epoch: 20 -> Test Accuracy: 32.22
[21, 60] loss: 1.736
[21, 120] loss: 1.718
[21, 180] loss: 1.704
[21, 240] loss: 1.719
[21, 300] loss: 1.697
[21, 360] loss: 1.698
Epoch: 21 -> Loss: 1.66823065281
Epoch: 21 -> Test Accuracy: 34.98
[22, 60] loss: 1.688
[22, 120] loss: 1.687
[22, 180] loss: 1.680
[22, 240] loss: 1.692
[22, 300] loss: 1.670
[22, 360] loss: 1.679
Epoch: 22 -> Loss: 1.74744164944
Epoch: 22 -> Test Accuracy: 35.23
[23, 60] loss: 1.665
[23, 120] loss: 1.687
[23, 180] loss: 1.670
[23, 240] loss: 1.674
[23, 300] loss: 1.658
[23, 360] loss: 1.690
Epoch: 23 -> Loss: 1.64546132088
Epoch: 23 -> Test Accuracy: 34.83
[24, 60] loss: 1.677
[24, 120] loss: 1.676
[24, 180] loss: 1.672
[24, 240] loss: 1.674
[24, 300] loss: 1.667
[24, 360] loss: 1.666
Epoch: 24 -> Loss: 1.57270264626
Epoch: 24 -> Test Accuracy: 35.0
[25, 60] loss: 1.675
[25, 120] loss: 1.666
[25, 180] loss: 1.668
[25, 240] loss: 1.655
[25, 300] loss: 1.664
[25, 360] loss: 1.669
Epoch: 25 -> Loss: 1.66615843773
Epoch: 25 -> Test Accuracy: 35.75
[26, 60] loss: 1.652
[26, 120] loss: 1.671
[26, 180] loss: 1.662
[26, 240] loss: 1.651
[26, 300] loss: 1.678
[26, 360] loss: 1.654
Epoch: 26 -> Loss: 1.68612635136
Epoch: 26 -> Test Accuracy: 35.25
[27, 60] loss: 1.668
[27, 120] loss: 1.662
[27, 180] loss: 1.663
[27, 240] loss: 1.645
[27, 300] loss: 1.653
[27, 360] loss: 1.679
Epoch: 27 -> Loss: 1.61806702614
Epoch: 27 -> Test Accuracy: 35.38
[28, 60] loss: 1.649
[28, 120] loss: 1.631
[28, 180] loss: 1.669
[28, 240] loss: 1.662
[28, 300] loss: 1.671
[28, 360] loss: 1.670
Epoch: 28 -> Loss: 1.65237653255
Epoch: 28 -> Test Accuracy: 35.88
[29, 60] loss: 1.657
[29, 120] loss: 1.661
[29, 180] loss: 1.666
[29, 240] loss: 1.649
[29, 300] loss: 1.648
[29, 360] loss: 1.645
Epoch: 29 -> Loss: 1.72619855404
Epoch: 29 -> Test Accuracy: 35.23
[30, 60] loss: 1.676
[30, 120] loss: 1.655
[30, 180] loss: 1.647
[30, 240] loss: 1.642
[30, 300] loss: 1.650
[30, 360] loss: 1.660
Epoch: 30 -> Loss: 1.58070516586
Epoch: 30 -> Test Accuracy: 35.45
[31, 60] loss: 1.658
[31, 120] loss: 1.620
[31, 180] loss: 1.655
[31, 240] loss: 1.662
[31, 300] loss: 1.651
[31, 360] loss: 1.659
Epoch: 31 -> Loss: 1.78566479683
Epoch: 31 -> Test Accuracy: 36.28
[32, 60] loss: 1.642
[32, 120] loss: 1.647
[32, 180] loss: 1.651
[32, 240] loss: 1.661
[32, 300] loss: 1.657
[32, 360] loss: 1.641
Epoch: 32 -> Loss: 1.61401367188
Epoch: 32 -> Test Accuracy: 35.42
[33, 60] loss: 1.628
[33, 120] loss: 1.667
[33, 180] loss: 1.657
[33, 240] loss: 1.666
[33, 300] loss: 1.646
[33, 360] loss: 1.651
Epoch: 33 -> Loss: 1.46038353443
Epoch: 33 -> Test Accuracy: 36.32
[34, 60] loss: 1.650
[34, 120] loss: 1.639
[34, 180] loss: 1.663
[34, 240] loss: 1.631
[34, 300] loss: 1.661
[34, 360] loss: 1.664
Epoch: 34 -> Loss: 1.63921451569
Epoch: 34 -> Test Accuracy: 36.24
[35, 60] loss: 1.637
[35, 120] loss: 1.658
[35, 180] loss: 1.670
[35, 240] loss: 1.641
[35, 300] loss: 1.654
[35, 360] loss: 1.638
Epoch: 35 -> Loss: 1.52031636238
Epoch: 35 -> Test Accuracy: 36.75
[36, 60] loss: 1.653
[36, 120] loss: 1.652
[36, 180] loss: 1.650
[36, 240] loss: 1.651
[36, 300] loss: 1.656
[36, 360] loss: 1.642
Epoch: 36 -> Loss: 1.73019337654
Epoch: 36 -> Test Accuracy: 35.95
[37, 60] loss: 1.640
[37, 120] loss: 1.655
[37, 180] loss: 1.636
[37, 240] loss: 1.650
[37, 300] loss: 1.634
[37, 360] loss: 1.662
Epoch: 37 -> Loss: 1.7520275116
Epoch: 37 -> Test Accuracy: 35.59
[38, 60] loss: 1.642
[38, 120] loss: 1.650
[38, 180] loss: 1.643
[38, 240] loss: 1.656
[38, 300] loss: 1.664
[38, 360] loss: 1.661
Epoch: 38 -> Loss: 1.50345671177
Epoch: 38 -> Test Accuracy: 35.78
[39, 60] loss: 1.621
[39, 120] loss: 1.617
[39, 180] loss: 1.660
[39, 240] loss: 1.665
[39, 300] loss: 1.663
[39, 360] loss: 1.644
Epoch: 39 -> Loss: 1.67437672615
Epoch: 39 -> Test Accuracy: 36.05
[40, 60] loss: 1.637
[40, 120] loss: 1.635
[40, 180] loss: 1.636
[40, 240] loss: 1.656
[40, 300] loss: 1.654
[40, 360] loss: 1.649
Epoch: 40 -> Loss: 1.69791388512
Epoch: 40 -> Test Accuracy: 35.94
[41, 60] loss: 1.631
[41, 120] loss: 1.613
[41, 180] loss: 1.611
[41, 240] loss: 1.585
[41, 300] loss: 1.596
[41, 360] loss: 1.597
Epoch: 41 -> Loss: 1.52905249596
Epoch: 41 -> Test Accuracy: 37.44
[42, 60] loss: 1.589
[42, 120] loss: 1.605
[42, 180] loss: 1.592
[42, 240] loss: 1.599
[42, 300] loss: 1.600
[42, 360] loss: 1.590
Epoch: 42 -> Loss: 1.51411664486
Epoch: 42 -> Test Accuracy: 37.54
[43, 60] loss: 1.576
[43, 120] loss: 1.552
[43, 180] loss: 1.592
[43, 240] loss: 1.590
[43, 300] loss: 1.566
[43, 360] loss: 1.588
Epoch: 43 -> Loss: 1.48796474934
Epoch: 43 -> Test Accuracy: 37.68
[44, 60] loss: 1.589
[44, 120] loss: 1.567
[44, 180] loss: 1.567
[44, 240] loss: 1.574
[44, 300] loss: 1.585
[44, 360] loss: 1.566
Epoch: 44 -> Loss: 1.55248272419
Epoch: 44 -> Test Accuracy: 37.51
[45, 60] loss: 1.573
[45, 120] loss: 1.583
[45, 180] loss: 1.589
[45, 240] loss: 1.558
[45, 300] loss: 1.576
[45, 360] loss: 1.584
Epoch: 45 -> Loss: 1.66815686226
Epoch: 45 -> Test Accuracy: 37.7
[46, 60] loss: 1.546
[46, 120] loss: 1.581
[46, 180] loss: 1.573
[46, 240] loss: 1.554
[46, 300] loss: 1.549
[46, 360] loss: 1.562
Epoch: 46 -> Loss: 1.74636232853
Epoch: 46 -> Test Accuracy: 38.27
[47, 60] loss: 1.559
[47, 120] loss: 1.578
[47, 180] loss: 1.564
[47, 240] loss: 1.559
[47, 300] loss: 1.554
[47, 360] loss: 1.561
Epoch: 47 -> Loss: 1.67219090462
Epoch: 47 -> Test Accuracy: 38.36
[48, 60] loss: 1.574
[48, 120] loss: 1.550
[48, 180] loss: 1.553
[48, 240] loss: 1.554
[48, 300] loss: 1.558
[48, 360] loss: 1.562
Epoch: 48 -> Loss: 1.62607038021
Epoch: 48 -> Test Accuracy: 38.48
[49, 60] loss: 1.541
[49, 120] loss: 1.557
[49, 180] loss: 1.554
[49, 240] loss: 1.560
[49, 300] loss: 1.560
[49, 360] loss: 1.567
Epoch: 49 -> Loss: 1.83787608147
Epoch: 49 -> Test Accuracy: 38.48
[50, 60] loss: 1.544
[50, 120] loss: 1.565
[50, 180] loss: 1.569
[50, 240] loss: 1.548
[50, 300] loss: 1.553
[50, 360] loss: 1.547
Epoch: 50 -> Loss: 1.45148348808
Epoch: 50 -> Test Accuracy: 38.48
[51, 60] loss: 1.569
[51, 120] loss: 1.558
[51, 180] loss: 1.549
[51, 240] loss: 1.546
[51, 300] loss: 1.535
[51, 360] loss: 1.531
Epoch: 51 -> Loss: 1.78328168392
Epoch: 51 -> Test Accuracy: 38.39
[52, 60] loss: 1.561
[52, 120] loss: 1.558
[52, 180] loss: 1.554
[52, 240] loss: 1.543
[52, 300] loss: 1.559
[52, 360] loss: 1.551
Epoch: 52 -> Loss: 1.56824326515
Epoch: 52 -> Test Accuracy: 38.17
[53, 60] loss: 1.556
[53, 120] loss: 1.569
[53, 180] loss: 1.543
[53, 240] loss: 1.555
[53, 300] loss: 1.557
[53, 360] loss: 1.560
Epoch: 53 -> Loss: 1.67066383362
Epoch: 53 -> Test Accuracy: 38.47
[54, 60] loss: 1.553
[54, 120] loss: 1.552
[54, 180] loss: 1.552
[54, 240] loss: 1.547
[54, 300] loss: 1.537
[54, 360] loss: 1.551
Epoch: 54 -> Loss: 1.50185477734
Epoch: 54 -> Test Accuracy: 38.33
[55, 60] loss: 1.561
[55, 120] loss: 1.526
[55, 180] loss: 1.549
[55, 240] loss: 1.554
[55, 300] loss: 1.539
[55, 360] loss: 1.548
Epoch: 55 -> Loss: 1.56229543686
Epoch: 55 -> Test Accuracy: 38.22
[56, 60] loss: 1.539
[56, 120] loss: 1.555
[56, 180] loss: 1.555
[56, 240] loss: 1.555
[56, 300] loss: 1.521
[56, 360] loss: 1.536
Epoch: 56 -> Loss: 1.52365350723
Epoch: 56 -> Test Accuracy: 38.33
[57, 60] loss: 1.546
[57, 120] loss: 1.544
[57, 180] loss: 1.536
[57, 240] loss: 1.555
[57, 300] loss: 1.545
[57, 360] loss: 1.534
Epoch: 57 -> Loss: 1.40506565571
Epoch: 57 -> Test Accuracy: 38.4
[58, 60] loss: 1.549
[58, 120] loss: 1.549
[58, 180] loss: 1.534
[58, 240] loss: 1.535
[58, 300] loss: 1.573
[58, 360] loss: 1.547
Epoch: 58 -> Loss: 1.54378581047
Epoch: 58 -> Test Accuracy: 38.48
[59, 60] loss: 1.550
[59, 120] loss: 1.531
[59, 180] loss: 1.543
[59, 240] loss: 1.548
[59, 300] loss: 1.546
[59, 360] loss: 1.544
Epoch: 59 -> Loss: 1.51378953457
Epoch: 59 -> Test Accuracy: 38.41
[60, 60] loss: 1.550
[60, 120] loss: 1.552
[60, 180] loss: 1.540
[60, 240] loss: 1.550
[60, 300] loss: 1.537
[60, 360] loss: 1.544
Epoch: 60 -> Loss: 1.68436467648
Epoch: 60 -> Test Accuracy: 38.28
[61, 60] loss: 1.536
[61, 120] loss: 1.549
[61, 180] loss: 1.535
[61, 240] loss: 1.544
[61, 300] loss: 1.546
[61, 360] loss: 1.542
Epoch: 61 -> Loss: 1.76267027855
Epoch: 61 -> Test Accuracy: 38.55
[62, 60] loss: 1.537
[62, 120] loss: 1.548
[62, 180] loss: 1.539
[62, 240] loss: 1.542
[62, 300] loss: 1.546
[62, 360] loss: 1.529
Epoch: 62 -> Loss: 1.42316508293
Epoch: 62 -> Test Accuracy: 38.47
[63, 60] loss: 1.543
[63, 120] loss: 1.547
[63, 180] loss: 1.526
[63, 240] loss: 1.539
[63, 300] loss: 1.526
[63, 360] loss: 1.545
Epoch: 63 -> Loss: 1.53315424919
Epoch: 63 -> Test Accuracy: 38.65
[64, 60] loss: 1.554
[64, 120] loss: 1.521
[64, 180] loss: 1.531
[64, 240] loss: 1.554
[64, 300] loss: 1.559
[64, 360] loss: 1.557
Epoch: 64 -> Loss: 1.57683038712
Epoch: 64 -> Test Accuracy: 38.31
[65, 60] loss: 1.536
[65, 120] loss: 1.544
[65, 180] loss: 1.519
[65, 240] loss: 1.539
[65, 300] loss: 1.543
[65, 360] loss: 1.538
Epoch: 65 -> Loss: 1.5305492878
Epoch: 65 -> Test Accuracy: 38.49
[66, 60] loss: 1.546
[66, 120] loss: 1.525
[66, 180] loss: 1.541
[66, 240] loss: 1.545
[66, 300] loss: 1.535
[66, 360] loss: 1.540
Epoch: 66 -> Loss: 1.58117127419
Epoch: 66 -> Test Accuracy: 38.48
[67, 60] loss: 1.538
[67, 120] loss: 1.539
[67, 180] loss: 1.542
[67, 240] loss: 1.540
[67, 300] loss: 1.548
[67, 360] loss: 1.549
Epoch: 67 -> Loss: 1.50874257088
Epoch: 67 -> Test Accuracy: 38.59
[68, 60] loss: 1.516
[68, 120] loss: 1.539
[68, 180] loss: 1.544
[68, 240] loss: 1.534
[68, 300] loss: 1.531
[68, 360] loss: 1.541
Epoch: 68 -> Loss: 1.54752898216
Epoch: 68 -> Test Accuracy: 38.49
[69, 60] loss: 1.510
[69, 120] loss: 1.540
[69, 180] loss: 1.557
[69, 240] loss: 1.539
[69, 300] loss: 1.539
[69, 360] loss: 1.555
Epoch: 69 -> Loss: 1.28347504139
Epoch: 69 -> Test Accuracy: 38.78
[70, 60] loss: 1.546
[70, 120] loss: 1.540
[70, 180] loss: 1.525
[70, 240] loss: 1.528
[70, 300] loss: 1.533
[70, 360] loss: 1.524
Epoch: 70 -> Loss: 1.40877139568
Epoch: 70 -> Test Accuracy: 38.35
[71, 60] loss: 1.513
[71, 120] loss: 1.542
[71, 180] loss: 1.527
[71, 240] loss: 1.541
[71, 300] loss: 1.515
[71, 360] loss: 1.541
Epoch: 71 -> Loss: 1.47087216377
Epoch: 71 -> Test Accuracy: 38.35
[72, 60] loss: 1.545
[72, 120] loss: 1.528
[72, 180] loss: 1.547
[72, 240] loss: 1.541
[72, 300] loss: 1.549
[72, 360] loss: 1.530
Epoch: 72 -> Loss: 1.65349137783
Epoch: 72 -> Test Accuracy: 38.49
[73, 60] loss: 1.528
[73, 120] loss: 1.543
[73, 180] loss: 1.539
[73, 240] loss: 1.529
[73, 300] loss: 1.541
[73, 360] loss: 1.520
Epoch: 73 -> Loss: 1.44064891338
Epoch: 73 -> Test Accuracy: 38.76
[74, 60] loss: 1.519
[74, 120] loss: 1.541
[74, 180] loss: 1.524
[74, 240] loss: 1.537
[74, 300] loss: 1.524
[74, 360] loss: 1.533
Epoch: 74 -> Loss: 1.42219233513
Epoch: 74 -> Test Accuracy: 38.7
[75, 60] loss: 1.536
[75, 120] loss: 1.516
[75, 180] loss: 1.549
[75, 240] loss: 1.517
[75, 300] loss: 1.539
[75, 360] loss: 1.526
Epoch: 75 -> Loss: 1.5182813406
Epoch: 75 -> Test Accuracy: 38.65
[76, 60] loss: 1.551
[76, 120] loss: 1.532
[76, 180] loss: 1.551
[76, 240] loss: 1.526
[76, 300] loss: 1.520
[76, 360] loss: 1.530
Epoch: 76 -> Loss: 1.56579899788
Epoch: 76 -> Test Accuracy: 38.66
[77, 60] loss: 1.524
[77, 120] loss: 1.531
[77, 180] loss: 1.531
[77, 240] loss: 1.533
[77, 300] loss: 1.512
[77, 360] loss: 1.549
Epoch: 77 -> Loss: 1.382553339
Epoch: 77 -> Test Accuracy: 38.44
[78, 60] loss: 1.544
[78, 120] loss: 1.522
[78, 180] loss: 1.522
[78, 240] loss: 1.536
[78, 300] loss: 1.520
[78, 360] loss: 1.518
Epoch: 78 -> Loss: 1.4872071743
Epoch: 78 -> Test Accuracy: 38.69
[79, 60] loss: 1.527
[79, 120] loss: 1.524
[79, 180] loss: 1.548
[79, 240] loss: 1.495
[79, 300] loss: 1.531
[79, 360] loss: 1.535
Epoch: 79 -> Loss: 1.6630461216
Epoch: 79 -> Test Accuracy: 38.66
[80, 60] loss: 1.524
[80, 120] loss: 1.539
[80, 180] loss: 1.524
[80, 240] loss: 1.521
[80, 300] loss: 1.527
[80, 360] loss: 1.518
Epoch: 80 -> Loss: 1.40660357475
Epoch: 80 -> Test Accuracy: 38.56
[81, 60] loss: 1.534
[81, 120] loss: 1.524
[81, 180] loss: 1.523
[81, 240] loss: 1.541
[81, 300] loss: 1.539
[81, 360] loss: 1.513
Epoch: 81 -> Loss: 1.63141596317
Epoch: 81 -> Test Accuracy: 38.78
[82, 60] loss: 1.529
[82, 120] loss: 1.526
[82, 180] loss: 1.536
[82, 240] loss: 1.525
[82, 300] loss: 1.516
[82, 360] loss: 1.537
Epoch: 82 -> Loss: 1.54906511307
Epoch: 82 -> Test Accuracy: 38.61
[83, 60] loss: 1.516
[83, 120] loss: 1.523
[83, 180] loss: 1.533
[83, 240] loss: 1.550
[83, 300] loss: 1.528
[83, 360] loss: 1.548
Epoch: 83 -> Loss: 1.86146092415
Epoch: 83 -> Test Accuracy: 38.57
[84, 60] loss: 1.512
[84, 120] loss: 1.529
[84, 180] loss: 1.522
[84, 240] loss: 1.526
[84, 300] loss: 1.525
[84, 360] loss: 1.535
Epoch: 84 -> Loss: 1.56655955315
Epoch: 84 -> Test Accuracy: 38.65
[85, 60] loss: 1.535
[85, 120] loss: 1.540
[85, 180] loss: 1.522
[85, 240] loss: 1.534
[85, 300] loss: 1.548
[85, 360] loss: 1.516
Epoch: 85 -> Loss: 1.41678702831
Epoch: 85 -> Test Accuracy: 38.65
[86, 60] loss: 1.523
[86, 120] loss: 1.532
[86, 180] loss: 1.542
[86, 240] loss: 1.520
[86, 300] loss: 1.523
[86, 360] loss: 1.529
Epoch: 86 -> Loss: 1.42956662178
Epoch: 86 -> Test Accuracy: 38.94
[87, 60] loss: 1.527
[87, 120] loss: 1.527
[87, 180] loss: 1.542
[87, 240] loss: 1.530
[87, 300] loss: 1.509
[87, 360] loss: 1.536
Epoch: 87 -> Loss: 1.61188793182
Epoch: 87 -> Test Accuracy: 38.77
[88, 60] loss: 1.530
[88, 120] loss: 1.529
[88, 180] loss: 1.520
[88, 240] loss: 1.541
[88, 300] loss: 1.538
[88, 360] loss: 1.522
Epoch: 88 -> Loss: 1.42896151543
Epoch: 88 -> Test Accuracy: 38.6
[89, 60] loss: 1.528
[89, 120] loss: 1.526
[89, 180] loss: 1.549
[89, 240] loss: 1.510
[89, 300] loss: 1.519
[89, 360] loss: 1.518
Epoch: 89 -> Loss: 1.43834519386
Epoch: 89 -> Test Accuracy: 38.86
[90, 60] loss: 1.519
[90, 120] loss: 1.515
[90, 180] loss: 1.520
[90, 240] loss: 1.523
[90, 300] loss: 1.504
[90, 360] loss: 1.527
Epoch: 90 -> Loss: 1.6965328455
Epoch: 90 -> Test Accuracy: 38.68
[91, 60] loss: 1.523
[91, 120] loss: 1.529
[91, 180] loss: 1.537
[91, 240] loss: 1.526
[91, 300] loss: 1.534
[91, 360] loss: 1.510
Epoch: 91 -> Loss: 1.49568963051
Epoch: 91 -> Test Accuracy: 38.68
[92, 60] loss: 1.534
[92, 120] loss: 1.534
[92, 180] loss: 1.536
[92, 240] loss: 1.513
[92, 300] loss: 1.522
[92, 360] loss: 1.535
Epoch: 92 -> Loss: 1.63094496727
Epoch: 92 -> Test Accuracy: 38.84
[93, 60] loss: 1.528
[93, 120] loss: 1.522
[93, 180] loss: 1.515
[93, 240] loss: 1.535
[93, 300] loss: 1.524
[93, 360] loss: 1.529
Epoch: 93 -> Loss: 1.35829985142
Epoch: 93 -> Test Accuracy: 38.59
[94, 60] loss: 1.528
[94, 120] loss: 1.512
[94, 180] loss: 1.518
[94, 240] loss: 1.519
[94, 300] loss: 1.543
[94, 360] loss: 1.532
Epoch: 94 -> Loss: 1.53057134151
Epoch: 94 -> Test Accuracy: 38.54
[95, 60] loss: 1.519
[95, 120] loss: 1.532
[95, 180] loss: 1.519
[95, 240] loss: 1.538
[95, 300] loss: 1.525
[95, 360] loss: 1.513
Epoch: 95 -> Loss: 1.56014883518
Epoch: 95 -> Test Accuracy: 38.93
[96, 60] loss: 1.526
[96, 120] loss: 1.506
[96, 180] loss: 1.518
[96, 240] loss: 1.514
[96, 300] loss: 1.505
[96, 360] loss: 1.518
Epoch: 96 -> Loss: 1.57741713524
Epoch: 96 -> Test Accuracy: 38.83
[97, 60] loss: 1.518
[97, 120] loss: 1.513
[97, 180] loss: 1.537
[97, 240] loss: 1.508
[97, 300] loss: 1.525
[97, 360] loss: 1.532
Epoch: 97 -> Loss: 1.39256501198
Epoch: 97 -> Test Accuracy: 39.0
[98, 60] loss: 1.509
[98, 120] loss: 1.503
[98, 180] loss: 1.532
[98, 240] loss: 1.503
[98, 300] loss: 1.518
[98, 360] loss: 1.534
Epoch: 98 -> Loss: 1.58949923515
Epoch: 98 -> Test Accuracy: 38.64
[99, 60] loss: 1.506
[99, 120] loss: 1.527
[99, 180] loss: 1.523
[99, 240] loss: 1.520
[99, 300] loss: 1.511
[99, 360] loss: 1.518
Epoch: 99 -> Loss: 1.3212211132
Epoch: 99 -> Test Accuracy: 38.87
[100, 60] loss: 1.531
[100, 120] loss: 1.494
[100, 180] loss: 1.527
[100, 240] loss: 1.519
[100, 300] loss: 1.531
[100, 360] loss: 1.524
Epoch: 100 -> Loss: 1.61050927639
Epoch: 100 -> Test Accuracy: 39.12
Finished Training
In [9]:
# train ConvClassifiers on feature map of net_3block
conv_block5_loss_log, _, conv_block5_test_accuracy_log, _, _ = tr.train_all_blocks(5, 10, [0.1, 0.02, 0.004, 0.0008], 
    [35, 70, 85, 100], 0.9, 5e-4, net_block5, criterion, trainloader, None, testloader, use_ConvClassifier=True) 
[1, 60] loss: 1.352
[1, 120] loss: 1.061
[1, 180] loss: 0.948
[1, 240] loss: 0.928
[1, 300] loss: 0.862
[1, 360] loss: 0.831
Epoch: 1 -> Loss: 0.759939789772
Epoch: 1 -> Test Accuracy: 68.27
[2, 60] loss: 0.776
[2, 120] loss: 0.762
[2, 180] loss: 0.733
[2, 240] loss: 0.739
[2, 300] loss: 0.712
[2, 360] loss: 0.707
Epoch: 2 -> Loss: 0.522010505199
Epoch: 2 -> Test Accuracy: 74.42
[3, 60] loss: 0.652
[3, 120] loss: 0.656
[3, 180] loss: 0.670
[3, 240] loss: 0.644
[3, 300] loss: 0.668
[3, 360] loss: 0.649
Epoch: 3 -> Loss: 0.641436398029
Epoch: 3 -> Test Accuracy: 75.58
[4, 60] loss: 0.594
[4, 120] loss: 0.609
[4, 180] loss: 0.618
[4, 240] loss: 0.615
[4, 300] loss: 0.610
[4, 360] loss: 0.612
Epoch: 4 -> Loss: 0.61285841465
Epoch: 4 -> Test Accuracy: 75.85
[5, 60] loss: 0.570
[5, 120] loss: 0.588
[5, 180] loss: 0.560
[5, 240] loss: 0.563
[5, 300] loss: 0.579
[5, 360] loss: 0.582
Epoch: 5 -> Loss: 0.694158673286
Epoch: 5 -> Test Accuracy: 75.9
[6, 60] loss: 0.559
[6, 120] loss: 0.558
[6, 180] loss: 0.545
[6, 240] loss: 0.585
[6, 300] loss: 0.541
[6, 360] loss: 0.553
Epoch: 6 -> Loss: 0.620222210884
Epoch: 6 -> Test Accuracy: 78.7
[7, 60] loss: 0.528
[7, 120] loss: 0.532
[7, 180] loss: 0.532
[7, 240] loss: 0.534
[7, 300] loss: 0.535
[7, 360] loss: 0.544
Epoch: 7 -> Loss: 0.471471130848
Epoch: 7 -> Test Accuracy: 78.89
[8, 60] loss: 0.518
[8, 120] loss: 0.506
[8, 180] loss: 0.523
[8, 240] loss: 0.527
[8, 300] loss: 0.542
[8, 360] loss: 0.521
Epoch: 8 -> Loss: 0.526454925537
Epoch: 8 -> Test Accuracy: 79.92
[9, 60] loss: 0.491
[9, 120] loss: 0.508
[9, 180] loss: 0.511
[9, 240] loss: 0.520
[9, 300] loss: 0.513
[9, 360] loss: 0.520
Epoch: 9 -> Loss: 0.396468639374
Epoch: 9 -> Test Accuracy: 77.85
[10, 60] loss: 0.481
[10, 120] loss: 0.495
[10, 180] loss: 0.509
[10, 240] loss: 0.508
[10, 300] loss: 0.509
[10, 360] loss: 0.490
Epoch: 10 -> Loss: 0.374304443598
Epoch: 10 -> Test Accuracy: 77.9
[11, 60] loss: 0.478
[11, 120] loss: 0.501
[11, 180] loss: 0.484
[11, 240] loss: 0.510
[11, 300] loss: 0.499
[11, 360] loss: 0.491
Epoch: 11 -> Loss: 0.547458052635
Epoch: 11 -> Test Accuracy: 79.02
[12, 60] loss: 0.471
[12, 120] loss: 0.486
[12, 180] loss: 0.496
[12, 240] loss: 0.475
[12, 300] loss: 0.504
[12, 360] loss: 0.497
Epoch: 12 -> Loss: 0.513858675957
Epoch: 12 -> Test Accuracy: 79.76
[13, 60] loss: 0.451
[13, 120] loss: 0.459
[13, 180] loss: 0.501
[13, 240] loss: 0.472
[13, 300] loss: 0.472
[13, 360] loss: 0.495
Epoch: 13 -> Loss: 0.468185126781
Epoch: 13 -> Test Accuracy: 78.25
[14, 60] loss: 0.465
[14, 120] loss: 0.480
[14, 180] loss: 0.462
[14, 240] loss: 0.471
[14, 300] loss: 0.478
[14, 360] loss: 0.485
Epoch: 14 -> Loss: 0.514837741852
Epoch: 14 -> Test Accuracy: 78.66
[15, 60] loss: 0.450
[15, 120] loss: 0.475
[15, 180] loss: 0.466
[15, 240] loss: 0.475
[15, 300] loss: 0.472
[15, 360] loss: 0.479
Epoch: 15 -> Loss: 0.471237182617
Epoch: 15 -> Test Accuracy: 80.28
[16, 60] loss: 0.446
[16, 120] loss: 0.446
[16, 180] loss: 0.456
[16, 240] loss: 0.481
[16, 300] loss: 0.480
[16, 360] loss: 0.489
Epoch: 16 -> Loss: 0.407373130322
Epoch: 16 -> Test Accuracy: 80.24
[17, 60] loss: 0.437
[17, 120] loss: 0.460
[17, 180] loss: 0.472
[17, 240] loss: 0.472
[17, 300] loss: 0.473
[17, 360] loss: 0.459
Epoch: 17 -> Loss: 0.510019779205
Epoch: 17 -> Test Accuracy: 81.08
[18, 60] loss: 0.438
[18, 120] loss: 0.441
[18, 180] loss: 0.471
[18, 240] loss: 0.481
[18, 300] loss: 0.467
[18, 360] loss: 0.464
Epoch: 18 -> Loss: 0.474419116974
Epoch: 18 -> Test Accuracy: 79.9
[19, 60] loss: 0.439
[19, 120] loss: 0.460
[19, 180] loss: 0.447
[19, 240] loss: 0.460
[19, 300] loss: 0.464
[19, 360] loss: 0.460
Epoch: 19 -> Loss: 0.493436008692
Epoch: 19 -> Test Accuracy: 80.47
[20, 60] loss: 0.428
[20, 120] loss: 0.444
[20, 180] loss: 0.455
[20, 240] loss: 0.460
[20, 300] loss: 0.456
[20, 360] loss: 0.476
Epoch: 20 -> Loss: 0.458377212286
Epoch: 20 -> Test Accuracy: 79.46
[21, 60] loss: 0.446
[21, 120] loss: 0.442
[21, 180] loss: 0.453
[21, 240] loss: 0.450
[21, 300] loss: 0.450
[21, 360] loss: 0.442
Epoch: 21 -> Loss: 0.395490467548
Epoch: 21 -> Test Accuracy: 79.5
[22, 60] loss: 0.417
[22, 120] loss: 0.430
[22, 180] loss: 0.466
[22, 240] loss: 0.441
[22, 300] loss: 0.462
[22, 360] loss: 0.479
Epoch: 22 -> Loss: 0.603022575378
Epoch: 22 -> Test Accuracy: 81.12
[23, 60] loss: 0.436
[23, 120] loss: 0.420
[23, 180] loss: 0.430
[23, 240] loss: 0.457
[23, 300] loss: 0.456
[23, 360] loss: 0.460
Epoch: 23 -> Loss: 0.583153009415
Epoch: 23 -> Test Accuracy: 80.43
[24, 60] loss: 0.432
[24, 120] loss: 0.451
[24, 180] loss: 0.429
[24, 240] loss: 0.467
[24, 300] loss: 0.445
[24, 360] loss: 0.461
Epoch: 24 -> Loss: 0.404473781586
Epoch: 24 -> Test Accuracy: 80.75
[25, 60] loss: 0.420
[25, 120] loss: 0.424
[25, 180] loss: 0.449
[25, 240] loss: 0.457
[25, 300] loss: 0.448
[25, 360] loss: 0.464
Epoch: 25 -> Loss: 0.699390470982
Epoch: 25 -> Test Accuracy: 79.69
[26, 60] loss: 0.424
[26, 120] loss: 0.433
[26, 180] loss: 0.450
[26, 240] loss: 0.445
[26, 300] loss: 0.446
[26, 360] loss: 0.468
Epoch: 26 -> Loss: 0.464469283819
Epoch: 26 -> Test Accuracy: 80.1
[27, 60] loss: 0.433
[27, 120] loss: 0.423
[27, 180] loss: 0.420
[27, 240] loss: 0.451
[27, 300] loss: 0.445
[27, 360] loss: 0.460
Epoch: 27 -> Loss: 0.251999527216
Epoch: 27 -> Test Accuracy: 80.29
[28, 60] loss: 0.418
[28, 120] loss: 0.426
[28, 180] loss: 0.438
[28, 240] loss: 0.441
[28, 300] loss: 0.442
[28, 360] loss: 0.460
Epoch: 28 -> Loss: 0.329118907452
Epoch: 28 -> Test Accuracy: 81.82
[29, 60] loss: 0.411
[29, 120] loss: 0.421
[29, 180] loss: 0.445
[29, 240] loss: 0.423
[29, 300] loss: 0.450
[29, 360] loss: 0.466
Epoch: 29 -> Loss: 0.509974241257
Epoch: 29 -> Test Accuracy: 79.72
[30, 60] loss: 0.401
[30, 120] loss: 0.413
[30, 180] loss: 0.446
[30, 240] loss: 0.453
[30, 300] loss: 0.464
[30, 360] loss: 0.441
Epoch: 30 -> Loss: 0.528644680977
Epoch: 30 -> Test Accuracy: 81.07
[31, 60] loss: 0.411
[31, 120] loss: 0.429
[31, 180] loss: 0.420
[31, 240] loss: 0.447
[31, 300] loss: 0.464
[31, 360] loss: 0.473
Epoch: 31 -> Loss: 0.543130278587
Epoch: 31 -> Test Accuracy: 80.92
[32, 60] loss: 0.438
[32, 120] loss: 0.416
[32, 180] loss: 0.414
[32, 240] loss: 0.469
[32, 300] loss: 0.440
[32, 360] loss: 0.436
Epoch: 32 -> Loss: 0.521073997021
Epoch: 32 -> Test Accuracy: 80.44
[33, 60] loss: 0.431
[33, 120] loss: 0.417
[33, 180] loss: 0.445
[33, 240] loss: 0.429
[33, 300] loss: 0.454
[33, 360] loss: 0.450
Epoch: 33 -> Loss: 0.466006666422
Epoch: 33 -> Test Accuracy: 80.63
[34, 60] loss: 0.403
[34, 120] loss: 0.421
[34, 180] loss: 0.444
[34, 240] loss: 0.445
[34, 300] loss: 0.442
[34, 360] loss: 0.441
Epoch: 34 -> Loss: 0.361333429813
Epoch: 34 -> Test Accuracy: 80.83
[35, 60] loss: 0.396
[35, 120] loss: 0.424
[35, 180] loss: 0.431
[35, 240] loss: 0.427
[35, 300] loss: 0.432
[35, 360] loss: 0.439
Epoch: 35 -> Loss: 0.508682787418
Epoch: 35 -> Test Accuracy: 81.01
[36, 60] loss: 0.347
[36, 120] loss: 0.314
[36, 180] loss: 0.307
[36, 240] loss: 0.306
[36, 300] loss: 0.311
[36, 360] loss: 0.296
Epoch: 36 -> Loss: 0.330245882273
Epoch: 36 -> Test Accuracy: 84.61
[37, 60] loss: 0.283
[37, 120] loss: 0.278
[37, 180] loss: 0.277
[37, 240] loss: 0.269
[37, 300] loss: 0.279
[37, 360] loss: 0.280
Epoch: 37 -> Loss: 0.155964404345
Epoch: 37 -> Test Accuracy: 84.76
[38, 60] loss: 0.255
[38, 120] loss: 0.263
[38, 180] loss: 0.258
[38, 240] loss: 0.267
[38, 300] loss: 0.263
[38, 360] loss: 0.272
Epoch: 38 -> Loss: 0.415426552296
Epoch: 38 -> Test Accuracy: 85.03
[39, 60] loss: 0.247
[39, 120] loss: 0.247
[39, 180] loss: 0.257
[39, 240] loss: 0.259
[39, 300] loss: 0.256
[39, 360] loss: 0.251
Epoch: 39 -> Loss: 0.140538573265
Epoch: 39 -> Test Accuracy: 84.63
[40, 60] loss: 0.238
[40, 120] loss: 0.247
[40, 180] loss: 0.251
[40, 240] loss: 0.250
[40, 300] loss: 0.251
[40, 360] loss: 0.271
Epoch: 40 -> Loss: 0.245443612337
Epoch: 40 -> Test Accuracy: 84.15
[41, 60] loss: 0.232
[41, 120] loss: 0.232
[41, 180] loss: 0.249
[41, 240] loss: 0.250
[41, 300] loss: 0.259
[41, 360] loss: 0.261
Epoch: 41 -> Loss: 0.186827391386
Epoch: 41 -> Test Accuracy: 84.33
[42, 60] loss: 0.237
[42, 120] loss: 0.235
[42, 180] loss: 0.246
[42, 240] loss: 0.240
[42, 300] loss: 0.250
[42, 360] loss: 0.255
Epoch: 42 -> Loss: 0.281368166208
Epoch: 42 -> Test Accuracy: 84.08
[43, 60] loss: 0.235
[43, 120] loss: 0.248
[43, 180] loss: 0.240
[43, 240] loss: 0.253
[43, 300] loss: 0.245
[43, 360] loss: 0.251
Epoch: 43 -> Loss: 0.160958588123
Epoch: 43 -> Test Accuracy: 84.44
[44, 60] loss: 0.233
[44, 120] loss: 0.239
[44, 180] loss: 0.238
[44, 240] loss: 0.247
[44, 300] loss: 0.253
[44, 360] loss: 0.244
Epoch: 44 -> Loss: 0.334113538265
Epoch: 44 -> Test Accuracy: 83.81
[45, 60] loss: 0.227
[45, 120] loss: 0.227
[45, 180] loss: 0.242
[45, 240] loss: 0.259
[45, 300] loss: 0.248
[45, 360] loss: 0.240
Epoch: 45 -> Loss: 0.149308085442
Epoch: 45 -> Test Accuracy: 84.51
[46, 60] loss: 0.228
[46, 120] loss: 0.234
[46, 180] loss: 0.245
[46, 240] loss: 0.243
[46, 300] loss: 0.241
[46, 360] loss: 0.257
Epoch: 46 -> Loss: 0.253108352423
Epoch: 46 -> Test Accuracy: 83.79
[47, 60] loss: 0.224
[47, 120] loss: 0.233
[47, 180] loss: 0.245
[47, 240] loss: 0.252
[47, 300] loss: 0.253
[47, 360] loss: 0.255
Epoch: 47 -> Loss: 0.322635650635
Epoch: 47 -> Test Accuracy: 83.32
[48, 60] loss: 0.237
[48, 120] loss: 0.229
[48, 180] loss: 0.242
[48, 240] loss: 0.247
[48, 300] loss: 0.250
[48, 360] loss: 0.256
Epoch: 48 -> Loss: 0.182466894388
Epoch: 48 -> Test Accuracy: 84.26
[49, 60] loss: 0.232
[49, 120] loss: 0.235
[49, 180] loss: 0.232
[49, 240] loss: 0.246
[49, 300] loss: 0.249
[49, 360] loss: 0.251
Epoch: 49 -> Loss: 0.236679837108
Epoch: 49 -> Test Accuracy: 84.2
[50, 60] loss: 0.223
[50, 120] loss: 0.230
[50, 180] loss: 0.230
[50, 240] loss: 0.256
[50, 300] loss: 0.250
[50, 360] loss: 0.252
Epoch: 50 -> Loss: 0.367073625326
Epoch: 50 -> Test Accuracy: 83.6
[51, 60] loss: 0.222
[51, 120] loss: 0.233
[51, 180] loss: 0.244
[51, 240] loss: 0.232
[51, 300] loss: 0.261
[51, 360] loss: 0.252
Epoch: 51 -> Loss: 0.276985824108
Epoch: 51 -> Test Accuracy: 84.11
[52, 60] loss: 0.226
[52, 120] loss: 0.222
[52, 180] loss: 0.231
[52, 240] loss: 0.239
[52, 300] loss: 0.264
[52, 360] loss: 0.271
Epoch: 52 -> Loss: 0.399486720562
Epoch: 52 -> Test Accuracy: 84.36
[53, 60] loss: 0.222
[53, 120] loss: 0.225
[53, 180] loss: 0.237
[53, 240] loss: 0.247
[53, 300] loss: 0.242
[53, 360] loss: 0.264
Epoch: 53 -> Loss: 0.184724837542
Epoch: 53 -> Test Accuracy: 83.78
[54, 60] loss: 0.218
[54, 120] loss: 0.227
[54, 180] loss: 0.249
[54, 240] loss: 0.238
[54, 300] loss: 0.247
[54, 360] loss: 0.256
Epoch: 54 -> Loss: 0.230543702841
Epoch: 54 -> Test Accuracy: 83.59
[55, 60] loss: 0.219
[55, 120] loss: 0.232
[55, 180] loss: 0.250
[55, 240] loss: 0.236
[55, 300] loss: 0.246
[55, 360] loss: 0.246
Epoch: 55 -> Loss: 0.183805555105
Epoch: 55 -> Test Accuracy: 84.3
[56, 60] loss: 0.226
[56, 120] loss: 0.248
[56, 180] loss: 0.251
[56, 240] loss: 0.238
[56, 300] loss: 0.234
[56, 360] loss: 0.246
Epoch: 56 -> Loss: 0.223450392485
Epoch: 56 -> Test Accuracy: 84.09
[57, 60] loss: 0.219
[57, 120] loss: 0.221
[57, 180] loss: 0.235
[57, 240] loss: 0.237
[57, 300] loss: 0.253
[57, 360] loss: 0.253
Epoch: 57 -> Loss: 0.286977887154
Epoch: 57 -> Test Accuracy: 83.37
[58, 60] loss: 0.222
[58, 120] loss: 0.231
[58, 180] loss: 0.228
[58, 240] loss: 0.228
[58, 300] loss: 0.235
[58, 360] loss: 0.248
Epoch: 58 -> Loss: 0.26318192482
Epoch: 58 -> Test Accuracy: 83.87
[59, 60] loss: 0.218
[59, 120] loss: 0.238
[59, 180] loss: 0.242
[59, 240] loss: 0.221
[59, 300] loss: 0.243
[59, 360] loss: 0.240
Epoch: 59 -> Loss: 0.301249235868
Epoch: 59 -> Test Accuracy: 83.58
[60, 60] loss: 0.229
[60, 120] loss: 0.223
[60, 180] loss: 0.228
[60, 240] loss: 0.251
[60, 300] loss: 0.242
[60, 360] loss: 0.249
Epoch: 60 -> Loss: 0.272841185331
Epoch: 60 -> Test Accuracy: 83.42
[61, 60] loss: 0.217
[61, 120] loss: 0.230
[61, 180] loss: 0.231
[61, 240] loss: 0.245
[61, 300] loss: 0.245
[61, 360] loss: 0.248
Epoch: 61 -> Loss: 0.337931156158
Epoch: 61 -> Test Accuracy: 83.79
[62, 60] loss: 0.221
[62, 120] loss: 0.224
[62, 180] loss: 0.234
[62, 240] loss: 0.235
[62, 300] loss: 0.246
[62, 360] loss: 0.240
Epoch: 62 -> Loss: 0.36541262269
Epoch: 62 -> Test Accuracy: 83.66
[63, 60] loss: 0.218
[63, 120] loss: 0.225
[63, 180] loss: 0.248
[63, 240] loss: 0.229
[63, 300] loss: 0.241
[63, 360] loss: 0.250
Epoch: 63 -> Loss: 0.294481903315
Epoch: 63 -> Test Accuracy: 83.92
[64, 60] loss: 0.202
[64, 120] loss: 0.228
[64, 180] loss: 0.235
[64, 240] loss: 0.232
[64, 300] loss: 0.238
[64, 360] loss: 0.246
Epoch: 64 -> Loss: 0.326318830252
Epoch: 64 -> Test Accuracy: 83.68
[65, 60] loss: 0.204
[65, 120] loss: 0.223
[65, 180] loss: 0.238
[65, 240] loss: 0.249
[65, 300] loss: 0.254
[65, 360] loss: 0.240
Epoch: 65 -> Loss: 0.297684848309
Epoch: 65 -> Test Accuracy: 83.3
[66, 60] loss: 0.225
[66, 120] loss: 0.213
[66, 180] loss: 0.234
[66, 240] loss: 0.230
[66, 300] loss: 0.242
[66, 360] loss: 0.235
Epoch: 66 -> Loss: 0.362421035767
Epoch: 66 -> Test Accuracy: 83.9
[67, 60] loss: 0.220
[67, 120] loss: 0.238
[67, 180] loss: 0.236
[67, 240] loss: 0.232
[67, 300] loss: 0.247
[67, 360] loss: 0.220
Epoch: 67 -> Loss: 0.27500808239
Epoch: 67 -> Test Accuracy: 83.92
[68, 60] loss: 0.216
[68, 120] loss: 0.218
[68, 180] loss: 0.230
[68, 240] loss: 0.237
[68, 300] loss: 0.242
[68, 360] loss: 0.246
Epoch: 68 -> Loss: 0.251587361097
Epoch: 68 -> Test Accuracy: 83.74
[69, 60] loss: 0.212
[69, 120] loss: 0.228
[69, 180] loss: 0.226
[69, 240] loss: 0.239
[69, 300] loss: 0.237
[69, 360] loss: 0.238
Epoch: 69 -> Loss: 0.279275119305
Epoch: 69 -> Test Accuracy: 83.69
[70, 60] loss: 0.207
[70, 120] loss: 0.222
[70, 180] loss: 0.245
[70, 240] loss: 0.233
[70, 300] loss: 0.233
[70, 360] loss: 0.239
Epoch: 70 -> Loss: 0.242570310831
Epoch: 70 -> Test Accuracy: 83.99
[71, 60] loss: 0.177
[71, 120] loss: 0.160
[71, 180] loss: 0.162
[71, 240] loss: 0.157
[71, 300] loss: 0.159
[71, 360] loss: 0.147
Epoch: 71 -> Loss: 0.249945372343
Epoch: 71 -> Test Accuracy: 85.75
[72, 60] loss: 0.134
[72, 120] loss: 0.136
[72, 180] loss: 0.140
[72, 240] loss: 0.144
[72, 300] loss: 0.142
[72, 360] loss: 0.152
Epoch: 72 -> Loss: 0.185048639774
Epoch: 72 -> Test Accuracy: 85.67
[73, 60] loss: 0.137
[73, 120] loss: 0.128
[73, 180] loss: 0.135
[73, 240] loss: 0.134
[73, 300] loss: 0.128
[73, 360] loss: 0.146
Epoch: 73 -> Loss: 0.11365352571
Epoch: 73 -> Test Accuracy: 85.99
[74, 60] loss: 0.129
[74, 120] loss: 0.128
[74, 180] loss: 0.129
[74, 240] loss: 0.136
[74, 300] loss: 0.129
[74, 360] loss: 0.141
Epoch: 74 -> Loss: 0.118794694543
Epoch: 74 -> Test Accuracy: 85.53
[75, 60] loss: 0.119
[75, 120] loss: 0.130
[75, 180] loss: 0.129
[75, 240] loss: 0.127
[75, 300] loss: 0.125
[75, 360] loss: 0.131
Epoch: 75 -> Loss: 0.110058307648
Epoch: 75 -> Test Accuracy: 85.65
[76, 60] loss: 0.120
[76, 120] loss: 0.125
[76, 180] loss: 0.123
[76, 240] loss: 0.120
[76, 300] loss: 0.131
[76, 360] loss: 0.127
Epoch: 76 -> Loss: 0.138767778873
Epoch: 76 -> Test Accuracy: 85.72
[77, 60] loss: 0.122
[77, 120] loss: 0.118
[77, 180] loss: 0.119
[77, 240] loss: 0.122
[77, 300] loss: 0.132
[77, 360] loss: 0.131
Epoch: 77 -> Loss: 0.105447307229
Epoch: 77 -> Test Accuracy: 85.36
[78, 60] loss: 0.115
[78, 120] loss: 0.118
[78, 180] loss: 0.116
[78, 240] loss: 0.127
[78, 300] loss: 0.122
[78, 360] loss: 0.123
Epoch: 78 -> Loss: 0.103502966464
Epoch: 78 -> Test Accuracy: 85.48
[79, 60] loss: 0.115
[79, 120] loss: 0.113
[79, 180] loss: 0.114
[79, 240] loss: 0.116
[79, 300] loss: 0.125
[79, 360] loss: 0.121
Epoch: 79 -> Loss: 0.10041461885
Epoch: 79 -> Test Accuracy: 85.47
[80, 60] loss: 0.110
[80, 120] loss: 0.118
[80, 180] loss: 0.115
[80, 240] loss: 0.115
[80, 300] loss: 0.114
[80, 360] loss: 0.119
Epoch: 80 -> Loss: 0.0979191586375
Epoch: 80 -> Test Accuracy: 85.61
[81, 60] loss: 0.110
[81, 120] loss: 0.112
[81, 180] loss: 0.111
[81, 240] loss: 0.117
[81, 300] loss: 0.120
[81, 360] loss: 0.113
Epoch: 81 -> Loss: 0.0923889875412
Epoch: 81 -> Test Accuracy: 85.11
[82, 60] loss: 0.110
[82, 120] loss: 0.109
[82, 180] loss: 0.109
[82, 240] loss: 0.118
[82, 300] loss: 0.114
[82, 360] loss: 0.114
Epoch: 82 -> Loss: 0.19514246285
Epoch: 82 -> Test Accuracy: 85.32
[83, 60] loss: 0.107
[83, 120] loss: 0.109
[83, 180] loss: 0.111
[83, 240] loss: 0.107
[83, 300] loss: 0.122
[83, 360] loss: 0.114
Epoch: 83 -> Loss: 0.0596535205841
Epoch: 83 -> Test Accuracy: 85.24
[84, 60] loss: 0.106
[84, 120] loss: 0.107
[84, 180] loss: 0.107
[84, 240] loss: 0.115
[84, 300] loss: 0.115
[84, 360] loss: 0.119
Epoch: 84 -> Loss: 0.050937525928
Epoch: 84 -> Test Accuracy: 85.17
[85, 60] loss: 0.108
[85, 120] loss: 0.114
[85, 180] loss: 0.112
[85, 240] loss: 0.113
[85, 300] loss: 0.111
[85, 360] loss: 0.115
Epoch: 85 -> Loss: 0.100176349282
Epoch: 85 -> Test Accuracy: 84.99
[86, 60] loss: 0.101
[86, 120] loss: 0.096
[86, 180] loss: 0.091
[86, 240] loss: 0.096
[86, 300] loss: 0.099
[86, 360] loss: 0.101
Epoch: 86 -> Loss: 0.0698599889874
Epoch: 86 -> Test Accuracy: 85.47
[87, 60] loss: 0.094
[87, 120] loss: 0.092
[87, 180] loss: 0.096
[87, 240] loss: 0.100
[87, 300] loss: 0.092
[87, 360] loss: 0.096
Epoch: 87 -> Loss: 0.139446765184
Epoch: 87 -> Test Accuracy: 85.51
[88, 60] loss: 0.096
[88, 120] loss: 0.093
[88, 180] loss: 0.094
[88, 240] loss: 0.093
[88, 300] loss: 0.095
[88, 360] loss: 0.094
Epoch: 88 -> Loss: 0.0789198949933
Epoch: 88 -> Test Accuracy: 85.44
[89, 60] loss: 0.097
[89, 120] loss: 0.091
[89, 180] loss: 0.091
[89, 240] loss: 0.090
[89, 300] loss: 0.098
[89, 360] loss: 0.091
Epoch: 89 -> Loss: 0.14526823163
Epoch: 89 -> Test Accuracy: 85.68
[90, 60] loss: 0.088
[90, 120] loss: 0.091
[90, 180] loss: 0.089
[90, 240] loss: 0.095
[90, 300] loss: 0.095
[90, 360] loss: 0.094
Epoch: 90 -> Loss: 0.108555816114
Epoch: 90 -> Test Accuracy: 85.54
[91, 60] loss: 0.092
[91, 120] loss: 0.090
[91, 180] loss: 0.089
[91, 240] loss: 0.091
[91, 300] loss: 0.090
[91, 360] loss: 0.098
Epoch: 91 -> Loss: 0.0691407769918
Epoch: 91 -> Test Accuracy: 85.56
[92, 60] loss: 0.089
[92, 120] loss: 0.088
[92, 180] loss: 0.090
[92, 240] loss: 0.097
[92, 300] loss: 0.089
[92, 360] loss: 0.088
Epoch: 92 -> Loss: 0.112366870046
Epoch: 92 -> Test Accuracy: 85.45
[93, 60] loss: 0.091
[93, 120] loss: 0.089
[93, 180] loss: 0.086
[93, 240] loss: 0.088
[93, 300] loss: 0.094
[93, 360] loss: 0.093
Epoch: 93 -> Loss: 0.0774373561144
Epoch: 93 -> Test Accuracy: 85.54
[94, 60] loss: 0.087
[94, 120] loss: 0.092
[94, 180] loss: 0.085
[94, 240] loss: 0.088
[94, 300] loss: 0.085
[94, 360] loss: 0.091
Epoch: 94 -> Loss: 0.156728237867
Epoch: 94 -> Test Accuracy: 85.41
[95, 60] loss: 0.085
[95, 120] loss: 0.086
[95, 180] loss: 0.091
[95, 240] loss: 0.090
[95, 300] loss: 0.088
[95, 360] loss: 0.090
Epoch: 95 -> Loss: 0.162043347955
Epoch: 95 -> Test Accuracy: 85.32
[96, 60] loss: 0.087
[96, 120] loss: 0.087
[96, 180] loss: 0.088
[96, 240] loss: 0.089
[96, 300] loss: 0.089
[96, 360] loss: 0.093
Epoch: 96 -> Loss: 0.0902237519622
Epoch: 96 -> Test Accuracy: 85.45
[97, 60] loss: 0.090
[97, 120] loss: 0.091
[97, 180] loss: 0.085
[97, 240] loss: 0.086
[97, 300] loss: 0.087
[97, 360] loss: 0.088
Epoch: 97 -> Loss: 0.119821645319
Epoch: 97 -> Test Accuracy: 85.47
[98, 60] loss: 0.086
[98, 120] loss: 0.085
[98, 180] loss: 0.086
[98, 240] loss: 0.087
[98, 300] loss: 0.090
[98, 360] loss: 0.094
Epoch: 98 -> Loss: 0.072603456676
Epoch: 98 -> Test Accuracy: 85.49
[99, 60] loss: 0.089
[99, 120] loss: 0.088
[99, 180] loss: 0.090
[99, 240] loss: 0.090
[99, 300] loss: 0.084
[99, 360] loss: 0.091
Epoch: 99 -> Loss: 0.125592559576
Epoch: 99 -> Test Accuracy: 85.5
[100, 60] loss: 0.082
[100, 120] loss: 0.085
[100, 180] loss: 0.092
[100, 240] loss: 0.089
[100, 300] loss: 0.083
[100, 360] loss: 0.090
Epoch: 100 -> Loss: 0.0403878800571
Epoch: 100 -> Test Accuracy: 85.28
Finished Training
[1, 60] loss: 0.950
[1, 120] loss: 0.655
[1, 180] loss: 0.596
[1, 240] loss: 0.551
[1, 300] loss: 0.519
[1, 360] loss: 0.509
Epoch: 1 -> Loss: 0.539218306541
Epoch: 1 -> Test Accuracy: 79.69
[2, 60] loss: 0.457
[2, 120] loss: 0.450
[2, 180] loss: 0.449
[2, 240] loss: 0.447
[2, 300] loss: 0.436
[2, 360] loss: 0.434
Epoch: 2 -> Loss: 0.409345060587
Epoch: 2 -> Test Accuracy: 81.97
[3, 60] loss: 0.406
[3, 120] loss: 0.418
[3, 180] loss: 0.387
[3, 240] loss: 0.396
[3, 300] loss: 0.388
[3, 360] loss: 0.403
Epoch: 3 -> Loss: 0.35332608223
Epoch: 3 -> Test Accuracy: 83.44
[4, 60] loss: 0.362
[4, 120] loss: 0.360
[4, 180] loss: 0.363
[4, 240] loss: 0.370
[4, 300] loss: 0.377
[4, 360] loss: 0.376
Epoch: 4 -> Loss: 0.392022073269
Epoch: 4 -> Test Accuracy: 83.95
[5, 60] loss: 0.338
[5, 120] loss: 0.350
[5, 180] loss: 0.347
[5, 240] loss: 0.348
[5, 300] loss: 0.337
[5, 360] loss: 0.361
Epoch: 5 -> Loss: 0.339683711529
Epoch: 5 -> Test Accuracy: 83.27
[6, 60] loss: 0.325
[6, 120] loss: 0.344
[6, 180] loss: 0.339
[6, 240] loss: 0.342
[6, 300] loss: 0.350
[6, 360] loss: 0.322
Epoch: 6 -> Loss: 0.357249289751
Epoch: 6 -> Test Accuracy: 84.05
[7, 60] loss: 0.301
[7, 120] loss: 0.341
[7, 180] loss: 0.335
[7, 240] loss: 0.321
[7, 300] loss: 0.316
[7, 360] loss: 0.320
Epoch: 7 -> Loss: 0.389253050089
Epoch: 7 -> Test Accuracy: 84.71
[8, 60] loss: 0.293
[8, 120] loss: 0.294
[8, 180] loss: 0.304
[8, 240] loss: 0.317
[8, 300] loss: 0.332
[8, 360] loss: 0.335
Epoch: 8 -> Loss: 0.474091142416
Epoch: 8 -> Test Accuracy: 84.65
[9, 60] loss: 0.288
[9, 120] loss: 0.294
[9, 180] loss: 0.309
[9, 240] loss: 0.317
[9, 300] loss: 0.311
[9, 360] loss: 0.336
Epoch: 9 -> Loss: 0.41759377718
Epoch: 9 -> Test Accuracy: 85.32
[10, 60] loss: 0.285
[10, 120] loss: 0.290
[10, 180] loss: 0.296
[10, 240] loss: 0.316
[10, 300] loss: 0.310
[10, 360] loss: 0.316
Epoch: 10 -> Loss: 0.338132470846
Epoch: 10 -> Test Accuracy: 84.9
[11, 60] loss: 0.277
[11, 120] loss: 0.300
[11, 180] loss: 0.285
[11, 240] loss: 0.319
[11, 300] loss: 0.296
[11, 360] loss: 0.308
Epoch: 11 -> Loss: 0.167863562703
Epoch: 11 -> Test Accuracy: 85.73
[12, 60] loss: 0.262
[12, 120] loss: 0.277
[12, 180] loss: 0.289
[12, 240] loss: 0.327
[12, 300] loss: 0.299
[12, 360] loss: 0.292
Epoch: 12 -> Loss: 0.111307814717
Epoch: 12 -> Test Accuracy: 84.76
[13, 60] loss: 0.264
[13, 120] loss: 0.288
[13, 180] loss: 0.283
[13, 240] loss: 0.299
[13, 300] loss: 0.300
[13, 360] loss: 0.301
Epoch: 13 -> Loss: 0.252347409725
Epoch: 13 -> Test Accuracy: 85.5
[14, 60] loss: 0.260
[14, 120] loss: 0.279
[14, 180] loss: 0.284
[14, 240] loss: 0.301
[14, 300] loss: 0.297
[14, 360] loss: 0.294
Epoch: 14 -> Loss: 0.364129632711
Epoch: 14 -> Test Accuracy: 84.46
[15, 60] loss: 0.268
[15, 120] loss: 0.272
[15, 180] loss: 0.284
[15, 240] loss: 0.295
[15, 300] loss: 0.287
[15, 360] loss: 0.296
Epoch: 15 -> Loss: 0.400423526764
Epoch: 15 -> Test Accuracy: 84.29
[16, 60] loss: 0.255
[16, 120] loss: 0.280
[16, 180] loss: 0.272
[16, 240] loss: 0.281
[16, 300] loss: 0.276
[16, 360] loss: 0.303
Epoch: 16 -> Loss: 0.329053431749
Epoch: 16 -> Test Accuracy: 85.5
[17, 60] loss: 0.263
[17, 120] loss: 0.268
[17, 180] loss: 0.287
[17, 240] loss: 0.278
[17, 300] loss: 0.294
[17, 360] loss: 0.287
Epoch: 17 -> Loss: 0.170243829489
Epoch: 17 -> Test Accuracy: 85.22
[18, 60] loss: 0.253
[18, 120] loss: 0.269
[18, 180] loss: 0.283
[18, 240] loss: 0.272
[18, 300] loss: 0.280
[18, 360] loss: 0.291
Epoch: 18 -> Loss: 0.35972815752
Epoch: 18 -> Test Accuracy: 84.89
[19, 60] loss: 0.256
[19, 120] loss: 0.259
[19, 180] loss: 0.262
[19, 240] loss: 0.292
[19, 300] loss: 0.287
[19, 360] loss: 0.278
Epoch: 19 -> Loss: 0.249232262373
Epoch: 19 -> Test Accuracy: 85.28
[20, 60] loss: 0.262
[20, 120] loss: 0.264
[20, 180] loss: 0.276
[20, 240] loss: 0.286
[20, 300] loss: 0.293
[20, 360] loss: 0.278
Epoch: 20 -> Loss: 0.347967594862
Epoch: 20 -> Test Accuracy: 85.64
[21, 60] loss: 0.253
[21, 120] loss: 0.248
[21, 180] loss: 0.279
[21, 240] loss: 0.271
[21, 300] loss: 0.285
[21, 360] loss: 0.283
Epoch: 21 -> Loss: 0.398791998625
Epoch: 21 -> Test Accuracy: 86.31
[22, 60] loss: 0.245
[22, 120] loss: 0.239
[22, 180] loss: 0.264
[22, 240] loss: 0.284
[22, 300] loss: 0.263
[22, 360] loss: 0.289
Epoch: 22 -> Loss: 0.446104854345
Epoch: 22 -> Test Accuracy: 85.01
[23, 60] loss: 0.250
[23, 120] loss: 0.256
[23, 180] loss: 0.272
[23, 240] loss: 0.287
[23, 300] loss: 0.289
[23, 360] loss: 0.280
Epoch: 23 -> Loss: 0.238490581512
Epoch: 23 -> Test Accuracy: 84.32
[24, 60] loss: 0.240
[24, 120] loss: 0.261
[24, 180] loss: 0.262
[24, 240] loss: 0.270
[24, 300] loss: 0.286
[24, 360] loss: 0.275
Epoch: 24 -> Loss: 0.255210250616
Epoch: 24 -> Test Accuracy: 85.9
[25, 60] loss: 0.247
[25, 120] loss: 0.258
[25, 180] loss: 0.276
[25, 240] loss: 0.271
[25, 300] loss: 0.284
[25, 360] loss: 0.284
Epoch: 25 -> Loss: 0.337270259857
Epoch: 25 -> Test Accuracy: 85.78
[26, 60] loss: 0.247
[26, 120] loss: 0.240
[26, 180] loss: 0.259
[26, 240] loss: 0.263
[26, 300] loss: 0.269
[26, 360] loss: 0.301
Epoch: 26 -> Loss: 0.226263910532
Epoch: 26 -> Test Accuracy: 85.14
[27, 60] loss: 0.245
[27, 120] loss: 0.253
[27, 180] loss: 0.262
[27, 240] loss: 0.261
[27, 300] loss: 0.274
[27, 360] loss: 0.263
Epoch: 27 -> Loss: 0.300476163626
Epoch: 27 -> Test Accuracy: 85.61
[28, 60] loss: 0.253
[28, 120] loss: 0.252
[28, 180] loss: 0.268
[28, 240] loss: 0.268
[28, 300] loss: 0.254
[28, 360] loss: 0.278
Epoch: 28 -> Loss: 0.455658853054
Epoch: 28 -> Test Accuracy: 85.36
[29, 60] loss: 0.241
[29, 120] loss: 0.248
[29, 180] loss: 0.258
[29, 240] loss: 0.259
[29, 300] loss: 0.266
[29, 360] loss: 0.289
Epoch: 29 -> Loss: 0.295382708311
Epoch: 29 -> Test Accuracy: 84.71
[30, 60] loss: 0.233
[30, 120] loss: 0.243
[30, 180] loss: 0.262
[30, 240] loss: 0.266
[30, 300] loss: 0.294
[30, 360] loss: 0.291
Epoch: 30 -> Loss: 0.398016661406
Epoch: 30 -> Test Accuracy: 85.43
[31, 60] loss: 0.225
[31, 120] loss: 0.252
[31, 180] loss: 0.258
[31, 240] loss: 0.271
[31, 300] loss: 0.279
[31, 360] loss: 0.273
Epoch: 31 -> Loss: 0.36989736557
Epoch: 31 -> Test Accuracy: 85.47
[32, 60] loss: 0.252
[32, 120] loss: 0.250
[32, 180] loss: 0.263
[32, 240] loss: 0.261
[32, 300] loss: 0.268
[32, 360] loss: 0.285
Epoch: 32 -> Loss: 0.167249187827
Epoch: 32 -> Test Accuracy: 85.71
[33, 60] loss: 0.224
[33, 120] loss: 0.245
[33, 180] loss: 0.255
[33, 240] loss: 0.273
[33, 300] loss: 0.268
[33, 360] loss: 0.285
Epoch: 33 -> Loss: 0.213450461626
Epoch: 33 -> Test Accuracy: 85.77
[34, 60] loss: 0.237
[34, 120] loss: 0.238
[34, 180] loss: 0.263
[34, 240] loss: 0.264
[34, 300] loss: 0.266
[34, 360] loss: 0.274
Epoch: 34 -> Loss: 0.227099820971
Epoch: 34 -> Test Accuracy: 84.46
[35, 60] loss: 0.242
[35, 120] loss: 0.243
[35, 180] loss: 0.260
[35, 240] loss: 0.254
[35, 300] loss: 0.281
[35, 360] loss: 0.285
Epoch: 35 -> Loss: 0.20040102303
Epoch: 35 -> Test Accuracy: 85.7
[36, 60] loss: 0.207
[36, 120] loss: 0.180
[36, 180] loss: 0.163
[36, 240] loss: 0.174
[36, 300] loss: 0.161
[36, 360] loss: 0.168
Epoch: 36 -> Loss: 0.178841218352
Epoch: 36 -> Test Accuracy: 88.54
[37, 60] loss: 0.141
[37, 120] loss: 0.140
[37, 180] loss: 0.151
[37, 240] loss: 0.141
[37, 300] loss: 0.140
[37, 360] loss: 0.148
Epoch: 37 -> Loss: 0.19337554276
Epoch: 37 -> Test Accuracy: 87.98
[38, 60] loss: 0.130
[38, 120] loss: 0.121
[38, 180] loss: 0.136
[38, 240] loss: 0.130
[38, 300] loss: 0.133
[38, 360] loss: 0.144
Epoch: 38 -> Loss: 0.13021209836
Epoch: 38 -> Test Accuracy: 88.26
[39, 60] loss: 0.115
[39, 120] loss: 0.115
[39, 180] loss: 0.125
[39, 240] loss: 0.125
[39, 300] loss: 0.119
[39, 360] loss: 0.126
Epoch: 39 -> Loss: 0.205182701349
Epoch: 39 -> Test Accuracy: 88.46
[40, 60] loss: 0.109
[40, 120] loss: 0.114
[40, 180] loss: 0.120
[40, 240] loss: 0.116
[40, 300] loss: 0.123
[40, 360] loss: 0.116
Epoch: 40 -> Loss: 0.126770943403
Epoch: 40 -> Test Accuracy: 87.99
[41, 60] loss: 0.112
[41, 120] loss: 0.110
[41, 180] loss: 0.112
[41, 240] loss: 0.115
[41, 300] loss: 0.107
[41, 360] loss: 0.114
Epoch: 41 -> Loss: 0.173801928759
Epoch: 41 -> Test Accuracy: 88.23
[42, 60] loss: 0.102
[42, 120] loss: 0.107
[42, 180] loss: 0.109
[42, 240] loss: 0.109
[42, 300] loss: 0.110
[42, 360] loss: 0.111
Epoch: 42 -> Loss: 0.237250089645
Epoch: 42 -> Test Accuracy: 87.73
[43, 60] loss: 0.098
[43, 120] loss: 0.100
[43, 180] loss: 0.107
[43, 240] loss: 0.104
[43, 300] loss: 0.111
[43, 360] loss: 0.115
Epoch: 43 -> Loss: 0.16972528398
Epoch: 43 -> Test Accuracy: 87.6
[44, 60] loss: 0.094
[44, 120] loss: 0.104
[44, 180] loss: 0.106
[44, 240] loss: 0.110
[44, 300] loss: 0.112
[44, 360] loss: 0.119
Epoch: 44 -> Loss: 0.175173729658
Epoch: 44 -> Test Accuracy: 87.76
[45, 60] loss: 0.095
[45, 120] loss: 0.100
[45, 180] loss: 0.098
[45, 240] loss: 0.102
[45, 300] loss: 0.105
[45, 360] loss: 0.112
Epoch: 45 -> Loss: 0.245339676738
Epoch: 45 -> Test Accuracy: 87.54
[46, 60] loss: 0.098
[46, 120] loss: 0.096
[46, 180] loss: 0.101
[46, 240] loss: 0.100
[46, 300] loss: 0.113
[46, 360] loss: 0.108
Epoch: 46 -> Loss: 0.222394153476
Epoch: 46 -> Test Accuracy: 87.71
[47, 60] loss: 0.098
[47, 120] loss: 0.105
[47, 180] loss: 0.104
[47, 240] loss: 0.103
[47, 300] loss: 0.102
[47, 360] loss: 0.112
Epoch: 47 -> Loss: 0.197726413608
Epoch: 47 -> Test Accuracy: 87.08
[48, 60] loss: 0.099
[48, 120] loss: 0.098
[48, 180] loss: 0.103
[48, 240] loss: 0.110
[48, 300] loss: 0.112
[48, 360] loss: 0.113
Epoch: 48 -> Loss: 0.312668889761
Epoch: 48 -> Test Accuracy: 87.26
[49, 60] loss: 0.087
[49, 120] loss: 0.103
[49, 180] loss: 0.108
[49, 240] loss: 0.108
[49, 300] loss: 0.113
[49, 360] loss: 0.109
Epoch: 49 -> Loss: 0.146272867918
Epoch: 49 -> Test Accuracy: 87.92
[50, 60] loss: 0.095
[50, 120] loss: 0.097
[50, 180] loss: 0.111
[50, 240] loss: 0.108
[50, 300] loss: 0.118
[50, 360] loss: 0.111
Epoch: 50 -> Loss: 0.1028393507
Epoch: 50 -> Test Accuracy: 87.6
[51, 60] loss: 0.092
[51, 120] loss: 0.096
[51, 180] loss: 0.104
[51, 240] loss: 0.102
[51, 300] loss: 0.120
[51, 360] loss: 0.113
Epoch: 51 -> Loss: 0.191755786538
Epoch: 51 -> Test Accuracy: 87.34
[52, 60] loss: 0.103
[52, 120] loss: 0.105
[52, 180] loss: 0.105
[52, 240] loss: 0.108
[52, 300] loss: 0.103
[52, 360] loss: 0.103
Epoch: 52 -> Loss: 0.156757131219
Epoch: 52 -> Test Accuracy: 87.54
[53, 60] loss: 0.096
[53, 120] loss: 0.107
[53, 180] loss: 0.109
[53, 240] loss: 0.110
[53, 300] loss: 0.106
[53, 360] loss: 0.116
Epoch: 53 -> Loss: 0.137719243765
Epoch: 53 -> Test Accuracy: 87.48
[54, 60] loss: 0.093
[54, 120] loss: 0.105
[54, 180] loss: 0.108
[54, 240] loss: 0.115
[54, 300] loss: 0.107
[54, 360] loss: 0.115
Epoch: 54 -> Loss: 0.1882378757
Epoch: 54 -> Test Accuracy: 87.12
[55, 60] loss: 0.091
[55, 120] loss: 0.100
[55, 180] loss: 0.100
[55, 240] loss: 0.102
[55, 300] loss: 0.109
[55, 360] loss: 0.124
Epoch: 55 -> Loss: 0.154113784432
Epoch: 55 -> Test Accuracy: 86.94
[56, 60] loss: 0.097
[56, 120] loss: 0.104
[56, 180] loss: 0.106
[56, 240] loss: 0.112
[56, 300] loss: 0.117
[56, 360] loss: 0.105
Epoch: 56 -> Loss: 0.191437244415
Epoch: 56 -> Test Accuracy: 86.89
[57, 60] loss: 0.092
[57, 120] loss: 0.096
[57, 180] loss: 0.100
[57, 240] loss: 0.108
[57, 300] loss: 0.118
[57, 360] loss: 0.119
Epoch: 57 -> Loss: 0.182171300054
Epoch: 57 -> Test Accuracy: 86.83
[58, 60] loss: 0.104
[58, 120] loss: 0.099
[58, 180] loss: 0.098
[58, 240] loss: 0.105
[58, 300] loss: 0.103
[58, 360] loss: 0.126
Epoch: 58 -> Loss: 0.0771923214197
Epoch: 58 -> Test Accuracy: 87.42
[59, 60] loss: 0.099
[59, 120] loss: 0.098
[59, 180] loss: 0.106
[59, 240] loss: 0.112
[59, 300] loss: 0.113
[59, 360] loss: 0.107
Epoch: 59 -> Loss: 0.108989581466
Epoch: 59 -> Test Accuracy: 87.63
[60, 60] loss: 0.101
[60, 120] loss: 0.094
[60, 180] loss: 0.095
[60, 240] loss: 0.102
[60, 300] loss: 0.110
[60, 360] loss: 0.118
Epoch: 60 -> Loss: 0.125832885504
Epoch: 60 -> Test Accuracy: 87.84
[61, 60] loss: 0.096
[61, 120] loss: 0.100
[61, 180] loss: 0.105
[61, 240] loss: 0.093
[61, 300] loss: 0.112
[61, 360] loss: 0.114
Epoch: 61 -> Loss: 0.0430082045496
Epoch: 61 -> Test Accuracy: 86.99
[62, 60] loss: 0.094
[62, 120] loss: 0.096
[62, 180] loss: 0.110
[62, 240] loss: 0.106
[62, 300] loss: 0.112
[62, 360] loss: 0.111
Epoch: 62 -> Loss: 0.100391790271
Epoch: 62 -> Test Accuracy: 87.25
[63, 60] loss: 0.098
[63, 120] loss: 0.100
[63, 180] loss: 0.099
[63, 240] loss: 0.102
[63, 300] loss: 0.102
[63, 360] loss: 0.113
Epoch: 63 -> Loss: 0.0969136804342
Epoch: 63 -> Test Accuracy: 87.5
[64, 60] loss: 0.093
[64, 120] loss: 0.104
[64, 180] loss: 0.095
[64, 240] loss: 0.105
[64, 300] loss: 0.101
[64, 360] loss: 0.106
Epoch: 64 -> Loss: 0.106285773218
Epoch: 64 -> Test Accuracy: 86.7
[65, 60] loss: 0.090
[65, 120] loss: 0.098
[65, 180] loss: 0.105
[65, 240] loss: 0.093
[65, 300] loss: 0.099
[65, 360] loss: 0.109
Epoch: 65 -> Loss: 0.0558515302837
Epoch: 65 -> Test Accuracy: 87.15
[66, 60] loss: 0.100
[66, 120] loss: 0.091
[66, 180] loss: 0.105
[66, 240] loss: 0.101
[66, 300] loss: 0.103
[66, 360] loss: 0.118
Epoch: 66 -> Loss: 0.10032980144
Epoch: 66 -> Test Accuracy: 87.0
[67, 60] loss: 0.097
[67, 120] loss: 0.098
[67, 180] loss: 0.089
[67, 240] loss: 0.101
[67, 300] loss: 0.105
[67, 360] loss: 0.113
Epoch: 67 -> Loss: 0.105195082724
Epoch: 67 -> Test Accuracy: 87.18
[68, 60] loss: 0.097
[68, 120] loss: 0.098
[68, 180] loss: 0.096
[68, 240] loss: 0.104
[68, 300] loss: 0.094
[68, 360] loss: 0.106
Epoch: 68 -> Loss: 0.166546106339
Epoch: 68 -> Test Accuracy: 87.57
[69, 60] loss: 0.093
[69, 120] loss: 0.095
[69, 180] loss: 0.089
[69, 240] loss: 0.114
[69, 300] loss: 0.113
[69, 360] loss: 0.114
Epoch: 69 -> Loss: 0.0638231262565
Epoch: 69 -> Test Accuracy: 86.86
[70, 60] loss: 0.099
[70, 120] loss: 0.098
[70, 180] loss: 0.098
[70, 240] loss: 0.107
[70, 300] loss: 0.112
[70, 360] loss: 0.110
Epoch: 70 -> Loss: 0.181370839477
Epoch: 70 -> Test Accuracy: 87.54
[71, 60] loss: 0.079
[71, 120] loss: 0.070
[71, 180] loss: 0.068
[71, 240] loss: 0.060
[71, 300] loss: 0.059
[71, 360] loss: 0.057
Epoch: 71 -> Loss: 0.129561260343
Epoch: 71 -> Test Accuracy: 88.99
[72, 60] loss: 0.053
[72, 120] loss: 0.052
[72, 180] loss: 0.053
[72, 240] loss: 0.047
[72, 300] loss: 0.051
[72, 360] loss: 0.050
Epoch: 72 -> Loss: 0.0847681388259
Epoch: 72 -> Test Accuracy: 88.93
[73, 60] loss: 0.047
[73, 120] loss: 0.044
[73, 180] loss: 0.049
[73, 240] loss: 0.047
[73, 300] loss: 0.048
[73, 360] loss: 0.049
Epoch: 73 -> Loss: 0.113650344312
Epoch: 73 -> Test Accuracy: 89.19
[74, 60] loss: 0.045
[74, 120] loss: 0.046
[74, 180] loss: 0.041
[74, 240] loss: 0.044
[74, 300] loss: 0.045
[74, 360] loss: 0.041
Epoch: 74 -> Loss: 0.0344423241913
Epoch: 74 -> Test Accuracy: 88.99
[75, 60] loss: 0.041
[75, 120] loss: 0.045
[75, 180] loss: 0.039
[75, 240] loss: 0.041
[75, 300] loss: 0.042
[75, 360] loss: 0.041
Epoch: 75 -> Loss: 0.0303840301931
Epoch: 75 -> Test Accuracy: 88.75
[76, 60] loss: 0.038
[76, 120] loss: 0.040
[76, 180] loss: 0.041
[76, 240] loss: 0.041
[76, 300] loss: 0.041
[76, 360] loss: 0.036
Epoch: 76 -> Loss: 0.0474015362561
Epoch: 76 -> Test Accuracy: 88.83
[77, 60] loss: 0.037
[77, 120] loss: 0.037
[77, 180] loss: 0.036
[77, 240] loss: 0.036
[77, 300] loss: 0.039
[77, 360] loss: 0.040
Epoch: 77 -> Loss: 0.0596053190529
Epoch: 77 -> Test Accuracy: 88.81
[78, 60] loss: 0.035
[78, 120] loss: 0.035
[78, 180] loss: 0.038
[78, 240] loss: 0.036
[78, 300] loss: 0.041
[78, 360] loss: 0.038
Epoch: 78 -> Loss: 0.0439391285181
Epoch: 78 -> Test Accuracy: 88.74
[79, 60] loss: 0.034
[79, 120] loss: 0.034
[79, 180] loss: 0.035
[79, 240] loss: 0.035
[79, 300] loss: 0.037
[79, 360] loss: 0.036
Epoch: 79 -> Loss: 0.044279973954
Epoch: 79 -> Test Accuracy: 88.83
[80, 60] loss: 0.034
[80, 120] loss: 0.034
[80, 180] loss: 0.032
[80, 240] loss: 0.034
[80, 300] loss: 0.035
[80, 360] loss: 0.038
Epoch: 80 -> Loss: 0.0172802563757
Epoch: 80 -> Test Accuracy: 88.64
[81, 60] loss: 0.033
[81, 120] loss: 0.031
[81, 180] loss: 0.031
[81, 240] loss: 0.033
[81, 300] loss: 0.031
[81, 360] loss: 0.037
Epoch: 81 -> Loss: 0.026349067688
Epoch: 81 -> Test Accuracy: 88.83
[82, 60] loss: 0.031
[82, 120] loss: 0.031
[82, 180] loss: 0.032
[82, 240] loss: 0.034
[82, 300] loss: 0.033
[82, 360] loss: 0.030
Epoch: 82 -> Loss: 0.020774345845
Epoch: 82 -> Test Accuracy: 88.91
[83, 60] loss: 0.029
[83, 120] loss: 0.030
[83, 180] loss: 0.031
[83, 240] loss: 0.033
[83, 300] loss: 0.033
[83, 360] loss: 0.037
Epoch: 83 -> Loss: 0.015046775341
Epoch: 83 -> Test Accuracy: 88.94
[84, 60] loss: 0.029
[84, 120] loss: 0.030
[84, 180] loss: 0.030
[84, 240] loss: 0.033
[84, 300] loss: 0.031
[84, 360] loss: 0.031
Epoch: 84 -> Loss: 0.0233301632106
Epoch: 84 -> Test Accuracy: 88.85
[85, 60] loss: 0.029
[85, 120] loss: 0.030
[85, 180] loss: 0.030
[85, 240] loss: 0.031
[85, 300] loss: 0.031
[85, 360] loss: 0.031
Epoch: 85 -> Loss: 0.0523966550827
Epoch: 85 -> Test Accuracy: 88.72
[86, 60] loss: 0.028
[86, 120] loss: 0.029
[86, 180] loss: 0.030
[86, 240] loss: 0.027
[86, 300] loss: 0.030
[86, 360] loss: 0.029
Epoch: 86 -> Loss: 0.0145873129368
Epoch: 86 -> Test Accuracy: 88.98
[87, 60] loss: 0.027
[87, 120] loss: 0.026
[87, 180] loss: 0.024
[87, 240] loss: 0.028
[87, 300] loss: 0.027
[87, 360] loss: 0.025
Epoch: 87 -> Loss: 0.025675997138
Epoch: 87 -> Test Accuracy: 88.84
[88, 60] loss: 0.028
[88, 120] loss: 0.028
[88, 180] loss: 0.028
[88, 240] loss: 0.025
[88, 300] loss: 0.027
[88, 360] loss: 0.026
Epoch: 88 -> Loss: 0.0189745239913
Epoch: 88 -> Test Accuracy: 88.86
[89, 60] loss: 0.026
[89, 120] loss: 0.027
[89, 180] loss: 0.025
[89, 240] loss: 0.027
[89, 300] loss: 0.025
[89, 360] loss: 0.029
Epoch: 89 -> Loss: 0.0349076017737
Epoch: 89 -> Test Accuracy: 88.94
[90, 60] loss: 0.025
[90, 120] loss: 0.026
[90, 180] loss: 0.026
[90, 240] loss: 0.024
[90, 300] loss: 0.028
[90, 360] loss: 0.025
Epoch: 90 -> Loss: 0.046495012939
Epoch: 90 -> Test Accuracy: 88.82
[91, 60] loss: 0.025
[91, 120] loss: 0.026
[91, 180] loss: 0.026
[91, 240] loss: 0.027
[91, 300] loss: 0.024
[91, 360] loss: 0.024
Epoch: 91 -> Loss: 0.0370255708694
Epoch: 91 -> Test Accuracy: 88.99
[92, 60] loss: 0.024
[92, 120] loss: 0.024
[92, 180] loss: 0.024
[92, 240] loss: 0.025
[92, 300] loss: 0.027
[92, 360] loss: 0.026
Epoch: 92 -> Loss: 0.0183320045471
Epoch: 92 -> Test Accuracy: 88.91
[93, 60] loss: 0.025
[93, 120] loss: 0.025
[93, 180] loss: 0.023
[93, 240] loss: 0.024
[93, 300] loss: 0.025
[93, 360] loss: 0.026
Epoch: 93 -> Loss: 0.0449894368649
Epoch: 93 -> Test Accuracy: 88.89
[94, 60] loss: 0.024
[94, 120] loss: 0.026
[94, 180] loss: 0.025
[94, 240] loss: 0.026
[94, 300] loss: 0.026
[94, 360] loss: 0.026
Epoch: 94 -> Loss: 0.0578770749271
Epoch: 94 -> Test Accuracy: 88.83
[95, 60] loss: 0.024
[95, 120] loss: 0.026
[95, 180] loss: 0.026
[95, 240] loss: 0.023
[95, 300] loss: 0.025
[95, 360] loss: 0.026
Epoch: 95 -> Loss: 0.0535115115345
Epoch: 95 -> Test Accuracy: 88.78
[96, 60] loss: 0.025
[96, 120] loss: 0.026
[96, 180] loss: 0.026
[96, 240] loss: 0.025
[96, 300] loss: 0.023
[96, 360] loss: 0.026
Epoch: 96 -> Loss: 0.0392332822084
Epoch: 96 -> Test Accuracy: 88.87
[97, 60] loss: 0.026
[97, 120] loss: 0.025
[97, 180] loss: 0.027
[97, 240] loss: 0.025
[97, 300] loss: 0.027
[97, 360] loss: 0.027
Epoch: 97 -> Loss: 0.0249798540026
Epoch: 97 -> Test Accuracy: 88.83
[98, 60] loss: 0.026
[98, 120] loss: 0.025
[98, 180] loss: 0.026
[98, 240] loss: 0.027
[98, 300] loss: 0.024
[98, 360] loss: 0.024
Epoch: 98 -> Loss: 0.0612790510058
Epoch: 98 -> Test Accuracy: 89.01
[99, 60] loss: 0.025
[99, 120] loss: 0.024
[99, 180] loss: 0.024
[99, 240] loss: 0.024
[99, 300] loss: 0.025
[99, 360] loss: 0.025
Epoch: 99 -> Loss: 0.023496394977
Epoch: 99 -> Test Accuracy: 89.03
[100, 60] loss: 0.025
[100, 120] loss: 0.024
[100, 180] loss: 0.025
[100, 240] loss: 0.022
[100, 300] loss: 0.024
[100, 360] loss: 0.025
Epoch: 100 -> Loss: 0.0243260748684
Epoch: 100 -> Test Accuracy: 89.0
Finished Training
[1, 60] loss: 0.889
[1, 120] loss: 0.654
[1, 180] loss: 0.592
[1, 240] loss: 0.572
[1, 300] loss: 0.549
[1, 360] loss: 0.529
Epoch: 1 -> Loss: 0.508641719818
Epoch: 1 -> Test Accuracy: 78.19
[2, 60] loss: 0.504
[2, 120] loss: 0.491
[2, 180] loss: 0.490
[2, 240] loss: 0.500
[2, 300] loss: 0.486
[2, 360] loss: 0.462
Epoch: 2 -> Loss: 0.384296238422
Epoch: 2 -> Test Accuracy: 80.43
[3, 60] loss: 0.456
[3, 120] loss: 0.444
[3, 180] loss: 0.467
[3, 240] loss: 0.450
[3, 300] loss: 0.457
[3, 360] loss: 0.465
Epoch: 3 -> Loss: 0.424328237772
Epoch: 3 -> Test Accuracy: 80.89
[4, 60] loss: 0.425
[4, 120] loss: 0.445
[4, 180] loss: 0.446
[4, 240] loss: 0.425
[4, 300] loss: 0.439
[4, 360] loss: 0.433
Epoch: 4 -> Loss: 0.433434545994
Epoch: 4 -> Test Accuracy: 80.85
[5, 60] loss: 0.407
[5, 120] loss: 0.410
[5, 180] loss: 0.428
[5, 240] loss: 0.416
[5, 300] loss: 0.431
[5, 360] loss: 0.409
Epoch: 5 -> Loss: 0.510099947453
Epoch: 5 -> Test Accuracy: 80.3
[6, 60] loss: 0.388
[6, 120] loss: 0.412
[6, 180] loss: 0.408
[6, 240] loss: 0.419
[6, 300] loss: 0.409
[6, 360] loss: 0.427
Epoch: 6 -> Loss: 0.52281999588
Epoch: 6 -> Test Accuracy: 81.66
[7, 60] loss: 0.385
[7, 120] loss: 0.390
[7, 180] loss: 0.401
[7, 240] loss: 0.405
[7, 300] loss: 0.405
[7, 360] loss: 0.413
Epoch: 7 -> Loss: 0.556824326515
Epoch: 7 -> Test Accuracy: 82.14
[8, 60] loss: 0.380
[8, 120] loss: 0.389
[8, 180] loss: 0.399
[8, 240] loss: 0.403
[8, 300] loss: 0.385
[8, 360] loss: 0.391
Epoch: 8 -> Loss: 0.341546297073
Epoch: 8 -> Test Accuracy: 82.13
[9, 60] loss: 0.375
[9, 120] loss: 0.389
[9, 180] loss: 0.380
[9, 240] loss: 0.383
[9, 300] loss: 0.404
[9, 360] loss: 0.411
Epoch: 9 -> Loss: 0.266939967871
Epoch: 9 -> Test Accuracy: 82.25
[10, 60] loss: 0.366
[10, 120] loss: 0.365
[10, 180] loss: 0.406
[10, 240] loss: 0.377
[10, 300] loss: 0.389
[10, 360] loss: 0.404
Epoch: 10 -> Loss: 0.356192320585
Epoch: 10 -> Test Accuracy: 82.64
[11, 60] loss: 0.365
[11, 120] loss: 0.394
[11, 180] loss: 0.374
[11, 240] loss: 0.365
[11, 300] loss: 0.395
[11, 360] loss: 0.370
Epoch: 11 -> Loss: 0.549596965313
Epoch: 11 -> Test Accuracy: 82.38
[12, 60] loss: 0.351
[12, 120] loss: 0.371
[12, 180] loss: 0.364
[12, 240] loss: 0.376
[12, 300] loss: 0.367
[12, 360] loss: 0.387
Epoch: 12 -> Loss: 0.352015554905
Epoch: 12 -> Test Accuracy: 81.7
[13, 60] loss: 0.376
[13, 120] loss: 0.362
[13, 180] loss: 0.367
[13, 240] loss: 0.375
[13, 300] loss: 0.399
[13, 360] loss: 0.379
Epoch: 13 -> Loss: 0.467985540628
Epoch: 13 -> Test Accuracy: 82.17
[14, 60] loss: 0.356
[14, 120] loss: 0.358
[14, 180] loss: 0.360
[14, 240] loss: 0.366
[14, 300] loss: 0.383
[14, 360] loss: 0.393
Epoch: 14 -> Loss: 0.297144144773
Epoch: 14 -> Test Accuracy: 82.74
[15, 60] loss: 0.344
[15, 120] loss: 0.356
[15, 180] loss: 0.362
[15, 240] loss: 0.383
[15, 300] loss: 0.366
[15, 360] loss: 0.395
Epoch: 15 -> Loss: 0.327633023262
Epoch: 15 -> Test Accuracy: 81.06
[16, 60] loss: 0.361
[16, 120] loss: 0.348
[16, 180] loss: 0.371
[16, 240] loss: 0.363
[16, 300] loss: 0.369
[16, 360] loss: 0.376
Epoch: 16 -> Loss: 0.45045799017
Epoch: 16 -> Test Accuracy: 82.44
[17, 60] loss: 0.339
[17, 120] loss: 0.366
[17, 180] loss: 0.362
[17, 240] loss: 0.360
[17, 300] loss: 0.372
[17, 360] loss: 0.378
Epoch: 17 -> Loss: 0.355867266655
Epoch: 17 -> Test Accuracy: 82.02
[18, 60] loss: 0.351
[18, 120] loss: 0.353
[18, 180] loss: 0.358
[18, 240] loss: 0.362
[18, 300] loss: 0.384
[18, 360] loss: 0.377
Epoch: 18 -> Loss: 0.317616194487
Epoch: 18 -> Test Accuracy: 81.78
[19, 60] loss: 0.338
[19, 120] loss: 0.361
[19, 180] loss: 0.359
[19, 240] loss: 0.351
[19, 300] loss: 0.366
[19, 360] loss: 0.381
Epoch: 19 -> Loss: 0.389977753162
Epoch: 19 -> Test Accuracy: 82.57
[20, 60] loss: 0.357
[20, 120] loss: 0.340
[20, 180] loss: 0.367
[20, 240] loss: 0.355
[20, 300] loss: 0.369
[20, 360] loss: 0.348
Epoch: 20 -> Loss: 0.376787424088
Epoch: 20 -> Test Accuracy: 82.24
[21, 60] loss: 0.344
[21, 120] loss: 0.364
[21, 180] loss: 0.348
[21, 240] loss: 0.368
[21, 300] loss: 0.371
[21, 360] loss: 0.370
Epoch: 21 -> Loss: 0.313180625439
Epoch: 21 -> Test Accuracy: 82.93
[22, 60] loss: 0.340
[22, 120] loss: 0.361
[22, 180] loss: 0.348
[22, 240] loss: 0.363
[22, 300] loss: 0.360
[22, 360] loss: 0.348
Epoch: 22 -> Loss: 0.509989202023
Epoch: 22 -> Test Accuracy: 81.91
[23, 60] loss: 0.350
[23, 120] loss: 0.350
[23, 180] loss: 0.359
[23, 240] loss: 0.354
[23, 300] loss: 0.384
[23, 360] loss: 0.354
Epoch: 23 -> Loss: 0.430521190166
Epoch: 23 -> Test Accuracy: 82.29
[24, 60] loss: 0.330
[24, 120] loss: 0.347
[24, 180] loss: 0.364
[24, 240] loss: 0.359
[24, 300] loss: 0.361
[24, 360] loss: 0.365
Epoch: 24 -> Loss: 0.409759223461
Epoch: 24 -> Test Accuracy: 82.45
[25, 60] loss: 0.351
[25, 120] loss: 0.348
[25, 180] loss: 0.348
[25, 240] loss: 0.360
[25, 300] loss: 0.355
[25, 360] loss: 0.363
Epoch: 25 -> Loss: 0.385100871325
Epoch: 25 -> Test Accuracy: 81.57
[26, 60] loss: 0.343
[26, 120] loss: 0.347
[26, 180] loss: 0.358
[26, 240] loss: 0.359
[26, 300] loss: 0.359
[26, 360] loss: 0.355
Epoch: 26 -> Loss: 0.188759878278
Epoch: 26 -> Test Accuracy: 81.85
[27, 60] loss: 0.319
[27, 120] loss: 0.355
[27, 180] loss: 0.363
[27, 240] loss: 0.342
[27, 300] loss: 0.365
[27, 360] loss: 0.365
Epoch: 27 -> Loss: 0.308426380157
Epoch: 27 -> Test Accuracy: 83.0
[28, 60] loss: 0.322
[28, 120] loss: 0.353
[28, 180] loss: 0.352
[28, 240] loss: 0.361
[28, 300] loss: 0.360
[28, 360] loss: 0.367
Epoch: 28 -> Loss: 0.321726232767
Epoch: 28 -> Test Accuracy: 82.94
[29, 60] loss: 0.328
[29, 120] loss: 0.343
[29, 180] loss: 0.330
[29, 240] loss: 0.351
[29, 300] loss: 0.364
[29, 360] loss: 0.384
Epoch: 29 -> Loss: 0.480570882559
Epoch: 29 -> Test Accuracy: 83.13
[30, 60] loss: 0.344
[30, 120] loss: 0.338
[30, 180] loss: 0.347
[30, 240] loss: 0.345
[30, 300] loss: 0.355
[30, 360] loss: 0.375
Epoch: 30 -> Loss: 0.275167644024
Epoch: 30 -> Test Accuracy: 82.77
[31, 60] loss: 0.323
[31, 120] loss: 0.353
[31, 180] loss: 0.337
[31, 240] loss: 0.359
[31, 300] loss: 0.358
[31, 360] loss: 0.350
Epoch: 31 -> Loss: 0.261042743921
Epoch: 31 -> Test Accuracy: 82.52
[32, 60] loss: 0.337
[32, 120] loss: 0.344
[32, 180] loss: 0.331
[32, 240] loss: 0.350
[32, 300] loss: 0.354
[32, 360] loss: 0.355
Epoch: 32 -> Loss: 0.396286129951
Epoch: 32 -> Test Accuracy: 82.54
[33, 60] loss: 0.337
[33, 120] loss: 0.335
[33, 180] loss: 0.335
[33, 240] loss: 0.366
[33, 300] loss: 0.353
[33, 360] loss: 0.350
Epoch: 33 -> Loss: 0.399610996246
Epoch: 33 -> Test Accuracy: 83.0
[34, 60] loss: 0.320
[34, 120] loss: 0.343
[34, 180] loss: 0.348
[34, 240] loss: 0.349
[34, 300] loss: 0.365
[34, 360] loss: 0.353
Epoch: 34 -> Loss: 0.399015009403
Epoch: 34 -> Test Accuracy: 82.46
[35, 60] loss: 0.344
[35, 120] loss: 0.347
[35, 180] loss: 0.353
[35, 240] loss: 0.343
[35, 300] loss: 0.374
[35, 360] loss: 0.349
Epoch: 35 -> Loss: 0.489450037479
Epoch: 35 -> Test Accuracy: 82.84
[36, 60] loss: 0.287
[36, 120] loss: 0.259
[36, 180] loss: 0.265
[36, 240] loss: 0.267
[36, 300] loss: 0.257
[36, 360] loss: 0.268
Epoch: 36 -> Loss: 0.261972039938
Epoch: 36 -> Test Accuracy: 84.77
[37, 60] loss: 0.242
[37, 120] loss: 0.240
[37, 180] loss: 0.253
[37, 240] loss: 0.241
[37, 300] loss: 0.230
[37, 360] loss: 0.228
Epoch: 37 -> Loss: 0.270760238171
Epoch: 37 -> Test Accuracy: 84.92
[38, 60] loss: 0.217
[38, 120] loss: 0.226
[38, 180] loss: 0.216
[38, 240] loss: 0.242
[38, 300] loss: 0.236
[38, 360] loss: 0.233
Epoch: 38 -> Loss: 0.24845738709
Epoch: 38 -> Test Accuracy: 84.63
[39, 60] loss: 0.211
[39, 120] loss: 0.212
[39, 180] loss: 0.224
[39, 240] loss: 0.224
[39, 300] loss: 0.220
[39, 360] loss: 0.235
Epoch: 39 -> Loss: 0.275641053915
Epoch: 39 -> Test Accuracy: 84.84
[40, 60] loss: 0.219
[40, 120] loss: 0.204
[40, 180] loss: 0.227
[40, 240] loss: 0.219
[40, 300] loss: 0.216
[40, 360] loss: 0.226
Epoch: 40 -> Loss: 0.239670708776
Epoch: 40 -> Test Accuracy: 84.82
[41, 60] loss: 0.212
[41, 120] loss: 0.212
[41, 180] loss: 0.213
[41, 240] loss: 0.212
[41, 300] loss: 0.202
[41, 360] loss: 0.215
Epoch: 41 -> Loss: 0.135684221983
Epoch: 41 -> Test Accuracy: 84.71
[42, 60] loss: 0.200
[42, 120] loss: 0.207
[42, 180] loss: 0.208
[42, 240] loss: 0.208
[42, 300] loss: 0.221
[42, 360] loss: 0.213
Epoch: 42 -> Loss: 0.231472179294
Epoch: 42 -> Test Accuracy: 84.22
[43, 60] loss: 0.210
[43, 120] loss: 0.208
[43, 180] loss: 0.207
[43, 240] loss: 0.198
[43, 300] loss: 0.213
[43, 360] loss: 0.234
Epoch: 43 -> Loss: 0.147014141083
Epoch: 43 -> Test Accuracy: 84.49
[44, 60] loss: 0.202
[44, 120] loss: 0.205
[44, 180] loss: 0.194
[44, 240] loss: 0.210
[44, 300] loss: 0.212
[44, 360] loss: 0.218
Epoch: 44 -> Loss: 0.213748186827
Epoch: 44 -> Test Accuracy: 84.87
[45, 60] loss: 0.202
[45, 120] loss: 0.203
[45, 180] loss: 0.209
[45, 240] loss: 0.201
[45, 300] loss: 0.216
[45, 360] loss: 0.210
Epoch: 45 -> Loss: 0.192213818431
Epoch: 45 -> Test Accuracy: 84.53
[46, 60] loss: 0.183
[46, 120] loss: 0.193
[46, 180] loss: 0.219
[46, 240] loss: 0.206
[46, 300] loss: 0.211
[46, 360] loss: 0.211
Epoch: 46 -> Loss: 0.204710766673
Epoch: 46 -> Test Accuracy: 84.63
[47, 60] loss: 0.189
[47, 120] loss: 0.203
[47, 180] loss: 0.200
[47, 240] loss: 0.202
[47, 300] loss: 0.211
[47, 360] loss: 0.210
Epoch: 47 -> Loss: 0.170248359442
Epoch: 47 -> Test Accuracy: 84.02
[48, 60] loss: 0.188
[48, 120] loss: 0.198
[48, 180] loss: 0.197
[48, 240] loss: 0.201
[48, 300] loss: 0.207
[48, 360] loss: 0.227
Epoch: 48 -> Loss: 0.326770961285
Epoch: 48 -> Test Accuracy: 84.21
[49, 60] loss: 0.192
[49, 120] loss: 0.196
[49, 180] loss: 0.202
[49, 240] loss: 0.201
[49, 300] loss: 0.207
[49, 360] loss: 0.207
Epoch: 49 -> Loss: 0.268282830715
Epoch: 49 -> Test Accuracy: 84.24
[50, 60] loss: 0.189
[50, 120] loss: 0.203
[50, 180] loss: 0.216
[50, 240] loss: 0.199
[50, 300] loss: 0.204
[50, 360] loss: 0.210
Epoch: 50 -> Loss: 0.210638567805
Epoch: 50 -> Test Accuracy: 84.04
[51, 60] loss: 0.194
[51, 120] loss: 0.204
[51, 180] loss: 0.205
[51, 240] loss: 0.201
[51, 300] loss: 0.219
[51, 360] loss: 0.197
Epoch: 51 -> Loss: 0.383303076029
Epoch: 51 -> Test Accuracy: 84.26
[52, 60] loss: 0.193
[52, 120] loss: 0.192
[52, 180] loss: 0.200
[52, 240] loss: 0.209
[52, 300] loss: 0.191
[52, 360] loss: 0.208
Epoch: 52 -> Loss: 0.351854145527
Epoch: 52 -> Test Accuracy: 84.34
[53, 60] loss: 0.197
[53, 120] loss: 0.190
[53, 180] loss: 0.191
[53, 240] loss: 0.211
[53, 300] loss: 0.202
[53, 360] loss: 0.213
Epoch: 53 -> Loss: 0.374006152153
Epoch: 53 -> Test Accuracy: 84.17
[54, 60] loss: 0.195
[54, 120] loss: 0.195
[54, 180] loss: 0.206
[54, 240] loss: 0.207
[54, 300] loss: 0.186
[54, 360] loss: 0.209
Epoch: 54 -> Loss: 0.195482745767
Epoch: 54 -> Test Accuracy: 83.98
[55, 60] loss: 0.191
[55, 120] loss: 0.193
[55, 180] loss: 0.195
[55, 240] loss: 0.201
[55, 300] loss: 0.208
[55, 360] loss: 0.207
Epoch: 55 -> Loss: 0.172643795609
Epoch: 55 -> Test Accuracy: 83.59
[56, 60] loss: 0.185
[56, 120] loss: 0.187
[56, 180] loss: 0.196
[56, 240] loss: 0.197
[56, 300] loss: 0.205
[56, 360] loss: 0.210
Epoch: 56 -> Loss: 0.169902250171
Epoch: 56 -> Test Accuracy: 84.19
[57, 60] loss: 0.192
[57, 120] loss: 0.197
[57, 180] loss: 0.190
[57, 240] loss: 0.202
[57, 300] loss: 0.198
[57, 360] loss: 0.204
Epoch: 57 -> Loss: 0.244485810399
Epoch: 57 -> Test Accuracy: 84.12
[58, 60] loss: 0.198
[58, 120] loss: 0.187
[58, 180] loss: 0.208
[58, 240] loss: 0.220
[58, 300] loss: 0.204
[58, 360] loss: 0.211
Epoch: 58 -> Loss: 0.179825395346
Epoch: 58 -> Test Accuracy: 84.47
[59, 60] loss: 0.177
[59, 120] loss: 0.195
[59, 180] loss: 0.203
[59, 240] loss: 0.196
[59, 300] loss: 0.193
[59, 360] loss: 0.221
Epoch: 59 -> Loss: 0.152445226908
Epoch: 59 -> Test Accuracy: 84.19
[60, 60] loss: 0.184
[60, 120] loss: 0.200
[60, 180] loss: 0.193
[60, 240] loss: 0.190
[60, 300] loss: 0.200
[60, 360] loss: 0.204
Epoch: 60 -> Loss: 0.235208660364
Epoch: 60 -> Test Accuracy: 84.67
[61, 60] loss: 0.188
[61, 120] loss: 0.180
[61, 180] loss: 0.198
[61, 240] loss: 0.188
[61, 300] loss: 0.205
[61, 360] loss: 0.198
Epoch: 61 -> Loss: 0.451281130314
Epoch: 61 -> Test Accuracy: 84.23
[62, 60] loss: 0.175
[62, 120] loss: 0.174
[62, 180] loss: 0.188
[62, 240] loss: 0.189
[62, 300] loss: 0.196
[62, 360] loss: 0.205
Epoch: 62 -> Loss: 0.247376725078
Epoch: 62 -> Test Accuracy: 84.43
[63, 60] loss: 0.196
[63, 120] loss: 0.197
[63, 180] loss: 0.186
[63, 240] loss: 0.194
[63, 300] loss: 0.200
[63, 360] loss: 0.202
Epoch: 63 -> Loss: 0.269571125507
Epoch: 63 -> Test Accuracy: 84.12
[64, 60] loss: 0.182
[64, 120] loss: 0.193
[64, 180] loss: 0.191
[64, 240] loss: 0.192
[64, 300] loss: 0.200
[64, 360] loss: 0.208
Epoch: 64 -> Loss: 0.206095099449
Epoch: 64 -> Test Accuracy: 84.52
[65, 60] loss: 0.176
[65, 120] loss: 0.176
[65, 180] loss: 0.189
[65, 240] loss: 0.208
[65, 300] loss: 0.196
[65, 360] loss: 0.201
Epoch: 65 -> Loss: 0.158252879977
Epoch: 65 -> Test Accuracy: 84.07
[66, 60] loss: 0.177
[66, 120] loss: 0.187
[66, 180] loss: 0.190
[66, 240] loss: 0.193
[66, 300] loss: 0.185
[66, 360] loss: 0.196
Epoch: 66 -> Loss: 0.140484184027
Epoch: 66 -> Test Accuracy: 84.02
[67, 60] loss: 0.170
[67, 120] loss: 0.174
[67, 180] loss: 0.188
[67, 240] loss: 0.186
[67, 300] loss: 0.189
[67, 360] loss: 0.201
Epoch: 67 -> Loss: 0.203672528267
Epoch: 67 -> Test Accuracy: 84.22
[68, 60] loss: 0.181
[68, 120] loss: 0.179
[68, 180] loss: 0.185
[68, 240] loss: 0.201
[68, 300] loss: 0.194
[68, 360] loss: 0.206
Epoch: 68 -> Loss: 0.202317878604
Epoch: 68 -> Test Accuracy: 84.31
[69, 60] loss: 0.183
[69, 120] loss: 0.176
[69, 180] loss: 0.183
[69, 240] loss: 0.189
[69, 300] loss: 0.201
[69, 360] loss: 0.200
Epoch: 69 -> Loss: 0.284465789795
Epoch: 69 -> Test Accuracy: 84.04
[70, 60] loss: 0.180
[70, 120] loss: 0.195
[70, 180] loss: 0.192
[70, 240] loss: 0.194
[70, 300] loss: 0.196
[70, 360] loss: 0.200
Epoch: 70 -> Loss: 0.233686923981
Epoch: 70 -> Test Accuracy: 83.19
[71, 60] loss: 0.161
[71, 120] loss: 0.140
[71, 180] loss: 0.143
[71, 240] loss: 0.137
[71, 300] loss: 0.127
[71, 360] loss: 0.134
Epoch: 71 -> Loss: 0.0922329425812
Epoch: 71 -> Test Accuracy: 85.51
[72, 60] loss: 0.123
[72, 120] loss: 0.124
[72, 180] loss: 0.128
[72, 240] loss: 0.121
[72, 300] loss: 0.132
[72, 360] loss: 0.133
Epoch: 72 -> Loss: 0.224260404706
Epoch: 72 -> Test Accuracy: 85.19
[73, 60] loss: 0.120
[73, 120] loss: 0.116
[73, 180] loss: 0.117
[73, 240] loss: 0.123
[73, 300] loss: 0.125
[73, 360] loss: 0.117
Epoch: 73 -> Loss: 0.129956111312
Epoch: 73 -> Test Accuracy: 85.27
[74, 60] loss: 0.103
[74, 120] loss: 0.106
[74, 180] loss: 0.114
[74, 240] loss: 0.120
[74, 300] loss: 0.115
[74, 360] loss: 0.119
Epoch: 74 -> Loss: 0.0741558670998
Epoch: 74 -> Test Accuracy: 85.06
[75, 60] loss: 0.110
[75, 120] loss: 0.103
[75, 180] loss: 0.113
[75, 240] loss: 0.103
[75, 300] loss: 0.106
[75, 360] loss: 0.114
Epoch: 75 -> Loss: 0.144316285849
Epoch: 75 -> Test Accuracy: 85.28
[76, 60] loss: 0.106
[76, 120] loss: 0.105
[76, 180] loss: 0.108
[76, 240] loss: 0.107
[76, 300] loss: 0.113
[76, 360] loss: 0.106
Epoch: 76 -> Loss: 0.132021829486
Epoch: 76 -> Test Accuracy: 85.41
[77, 60] loss: 0.101
[77, 120] loss: 0.115
[77, 180] loss: 0.105
[77, 240] loss: 0.103
[77, 300] loss: 0.100
[77, 360] loss: 0.102
Epoch: 77 -> Loss: 0.0991534739733
Epoch: 77 -> Test Accuracy: 84.98
[78, 60] loss: 0.103
[78, 120] loss: 0.095
[78, 180] loss: 0.103
[78, 240] loss: 0.104
[78, 300] loss: 0.105
[78, 360] loss: 0.104
Epoch: 78 -> Loss: 0.146122738719
Epoch: 78 -> Test Accuracy: 85.19
[79, 60] loss: 0.095
[79, 120] loss: 0.098
[79, 180] loss: 0.107
[79, 240] loss: 0.099
[79, 300] loss: 0.096
[79, 360] loss: 0.101
Epoch: 79 -> Loss: 0.133021309972
Epoch: 79 -> Test Accuracy: 85.07
[80, 60] loss: 0.096
[80, 120] loss: 0.096
[80, 180] loss: 0.101
[80, 240] loss: 0.105
[80, 300] loss: 0.094
[80, 360] loss: 0.104
Epoch: 80 -> Loss: 0.159720659256
Epoch: 80 -> Test Accuracy: 85.03
[81, 60] loss: 0.093
[81, 120] loss: 0.094
[81, 180] loss: 0.093
[81, 240] loss: 0.098
[81, 300] loss: 0.099
[81, 360] loss: 0.099
Epoch: 81 -> Loss: 0.158382326365
Epoch: 81 -> Test Accuracy: 85.46
[82, 60] loss: 0.095
[82, 120] loss: 0.090
[82, 180] loss: 0.093
[82, 240] loss: 0.091
[82, 300] loss: 0.093
[82, 360] loss: 0.095
Epoch: 82 -> Loss: 0.0606381893158
Epoch: 82 -> Test Accuracy: 85.38
[83, 60] loss: 0.090
[83, 120] loss: 0.092
[83, 180] loss: 0.097
[83, 240] loss: 0.097
[83, 300] loss: 0.100
[83, 360] loss: 0.100
Epoch: 83 -> Loss: 0.0515924468637
Epoch: 83 -> Test Accuracy: 85.29
[84, 60] loss: 0.091
[84, 120] loss: 0.091
[84, 180] loss: 0.097
[84, 240] loss: 0.090
[84, 300] loss: 0.094
[84, 360] loss: 0.095
Epoch: 84 -> Loss: 0.0481597967446
Epoch: 84 -> Test Accuracy: 85.14
[85, 60] loss: 0.083
[85, 120] loss: 0.092
[85, 180] loss: 0.101
[85, 240] loss: 0.089
[85, 300] loss: 0.099
[85, 360] loss: 0.085
Epoch: 85 -> Loss: 0.0471388734877
Epoch: 85 -> Test Accuracy: 85.06
[86, 60] loss: 0.082
[86, 120] loss: 0.081
[86, 180] loss: 0.083
[86, 240] loss: 0.084
[86, 300] loss: 0.081
[86, 360] loss: 0.079
Epoch: 86 -> Loss: 0.0917103737593
Epoch: 86 -> Test Accuracy: 85.27
[87, 60] loss: 0.081
[87, 120] loss: 0.077
[87, 180] loss: 0.086
[87, 240] loss: 0.082
[87, 300] loss: 0.080
[87, 360] loss: 0.081
Epoch: 87 -> Loss: 0.109203979373
Epoch: 87 -> Test Accuracy: 85.25
[88, 60] loss: 0.079
[88, 120] loss: 0.084
[88, 180] loss: 0.084
[88, 240] loss: 0.078
[88, 300] loss: 0.080
[88, 360] loss: 0.080
Epoch: 88 -> Loss: 0.0930551737547
Epoch: 88 -> Test Accuracy: 85.26
[89, 60] loss: 0.080
[89, 120] loss: 0.079
[89, 180] loss: 0.078
[89, 240] loss: 0.079
[89, 300] loss: 0.077
[89, 360] loss: 0.079
Epoch: 89 -> Loss: 0.089452907443
Epoch: 89 -> Test Accuracy: 85.32
[90, 60] loss: 0.078
[90, 120] loss: 0.077
[90, 180] loss: 0.073
[90, 240] loss: 0.076
[90, 300] loss: 0.071
[90, 360] loss: 0.076
Epoch: 90 -> Loss: 0.0825238227844
Epoch: 90 -> Test Accuracy: 85.39
[91, 60] loss: 0.072
[91, 120] loss: 0.077
[91, 180] loss: 0.076
[91, 240] loss: 0.075
[91, 300] loss: 0.080
[91, 360] loss: 0.078
Epoch: 91 -> Loss: 0.154678195715
Epoch: 91 -> Test Accuracy: 85.29
[92, 60] loss: 0.074
[92, 120] loss: 0.075
[92, 180] loss: 0.076
[92, 240] loss: 0.074
[92, 300] loss: 0.082
[92, 360] loss: 0.079
Epoch: 92 -> Loss: 0.0641233474016
Epoch: 92 -> Test Accuracy: 85.36
[93, 60] loss: 0.074
[93, 120] loss: 0.078
[93, 180] loss: 0.072
[93, 240] loss: 0.078
[93, 300] loss: 0.072
[93, 360] loss: 0.075
Epoch: 93 -> Loss: 0.0602846257389
Epoch: 93 -> Test Accuracy: 85.43
[94, 60] loss: 0.076
[94, 120] loss: 0.079
[94, 180] loss: 0.076
[94, 240] loss: 0.076
[94, 300] loss: 0.074
[94, 360] loss: 0.072
Epoch: 94 -> Loss: 0.0833013057709
Epoch: 94 -> Test Accuracy: 85.43
[95, 60] loss: 0.078
[95, 120] loss: 0.077
[95, 180] loss: 0.077
[95, 240] loss: 0.073
[95, 300] loss: 0.072
[95, 360] loss: 0.074
Epoch: 95 -> Loss: 0.0787304118276
Epoch: 95 -> Test Accuracy: 85.39
[96, 60] loss: 0.078
[96, 120] loss: 0.072
[96, 180] loss: 0.076
[96, 240] loss: 0.069
[96, 300] loss: 0.071
[96, 360] loss: 0.075
Epoch: 96 -> Loss: 0.0581420250237
Epoch: 96 -> Test Accuracy: 85.25
[97, 60] loss: 0.072
[97, 120] loss: 0.077
[97, 180] loss: 0.073
[97, 240] loss: 0.078
[97, 300] loss: 0.069
[97, 360] loss: 0.072
Epoch: 97 -> Loss: 0.0873824954033
Epoch: 97 -> Test Accuracy: 85.24
[98, 60] loss: 0.076
[98, 120] loss: 0.073
[98, 180] loss: 0.070
[98, 240] loss: 0.072
[98, 300] loss: 0.071
[98, 360] loss: 0.077
Epoch: 98 -> Loss: 0.108136489987
Epoch: 98 -> Test Accuracy: 85.39
[99, 60] loss: 0.071
[99, 120] loss: 0.069
[99, 180] loss: 0.073
[99, 240] loss: 0.071
[99, 300] loss: 0.074
[99, 360] loss: 0.071
Epoch: 99 -> Loss: 0.0875692218542
Epoch: 99 -> Test Accuracy: 85.34
[100, 60] loss: 0.070
[100, 120] loss: 0.075
[100, 180] loss: 0.065
[100, 240] loss: 0.072
[100, 300] loss: 0.075
[100, 360] loss: 0.077
Epoch: 100 -> Loss: 0.106583692133
Epoch: 100 -> Test Accuracy: 85.4
Finished Training
[1, 60] loss: 1.230
[1, 120] loss: 0.980
[1, 180] loss: 0.939
[1, 240] loss: 0.893
[1, 300] loss: 0.863
[1, 360] loss: 0.857
Epoch: 1 -> Loss: 0.650463223457
Epoch: 1 -> Test Accuracy: 65.72
[2, 60] loss: 0.830
[2, 120] loss: 0.812
[2, 180] loss: 0.803
[2, 240] loss: 0.768
[2, 300] loss: 0.772
[2, 360] loss: 0.765
Epoch: 2 -> Loss: 0.787874281406
Epoch: 2 -> Test Accuracy: 66.46
[3, 60] loss: 0.755
[3, 120] loss: 0.732
[3, 180] loss: 0.755
[3, 240] loss: 0.727
[3, 300] loss: 0.750
[3, 360] loss: 0.732
Epoch: 3 -> Loss: 0.760669112206
Epoch: 3 -> Test Accuracy: 68.46
[4, 60] loss: 0.730
[4, 120] loss: 0.719
[4, 180] loss: 0.743
[4, 240] loss: 0.722
[4, 300] loss: 0.713
[4, 360] loss: 0.717
Epoch: 4 -> Loss: 0.802625060081
Epoch: 4 -> Test Accuracy: 68.2
[5, 60] loss: 0.711
[5, 120] loss: 0.716
[5, 180] loss: 0.717
[5, 240] loss: 0.729
[5, 300] loss: 0.716
[5, 360] loss: 0.698
Epoch: 5 -> Loss: 0.886916339397
Epoch: 5 -> Test Accuracy: 69.89
[6, 60] loss: 0.679
[6, 120] loss: 0.713
[6, 180] loss: 0.700
[6, 240] loss: 0.700
[6, 300] loss: 0.697
[6, 360] loss: 0.688
Epoch: 6 -> Loss: 0.740843892097
Epoch: 6 -> Test Accuracy: 68.83
[7, 60] loss: 0.683
[7, 120] loss: 0.692
[7, 180] loss: 0.696
[7, 240] loss: 0.700
[7, 300] loss: 0.688
[7, 360] loss: 0.713
Epoch: 7 -> Loss: 0.778291881084
Epoch: 7 -> Test Accuracy: 70.97
[8, 60] loss: 0.684
[8, 120] loss: 0.664
[8, 180] loss: 0.707
[8, 240] loss: 0.689
[8, 300] loss: 0.683
[8, 360] loss: 0.702
Epoch: 8 -> Loss: 0.677375733852
Epoch: 8 -> Test Accuracy: 69.92
[9, 60] loss: 0.672
[9, 120] loss: 0.660
[9, 180] loss: 0.681
[9, 240] loss: 0.663
[9, 300] loss: 0.698
[9, 360] loss: 0.689
Epoch: 9 -> Loss: 0.838647007942
Epoch: 9 -> Test Accuracy: 68.99
[10, 60] loss: 0.657
[10, 120] loss: 0.669
[10, 180] loss: 0.682
[10, 240] loss: 0.682
[10, 300] loss: 0.686
[10, 360] loss: 0.682
Epoch: 10 -> Loss: 0.778439223766
Epoch: 10 -> Test Accuracy: 69.34
[11, 60] loss: 0.657
[11, 120] loss: 0.676
[11, 180] loss: 0.665
[11, 240] loss: 0.688
[11, 300] loss: 0.667
[11, 360] loss: 0.690
Epoch: 11 -> Loss: 0.775718033314
Epoch: 11 -> Test Accuracy: 70.35
[12, 60] loss: 0.660
[12, 120] loss: 0.679
[12, 180] loss: 0.658
[12, 240] loss: 0.700
[12, 300] loss: 0.669
[12, 360] loss: 0.687
Epoch: 12 -> Loss: 0.702473163605
Epoch: 12 -> Test Accuracy: 70.93
[13, 60] loss: 0.659
[13, 120] loss: 0.671
[13, 180] loss: 0.657
[13, 240] loss: 0.670
[13, 300] loss: 0.669
[13, 360] loss: 0.670
Epoch: 13 -> Loss: 0.753682732582
Epoch: 13 -> Test Accuracy: 70.62
[14, 60] loss: 0.638
[14, 120] loss: 0.675
[14, 180] loss: 0.654
[14, 240] loss: 0.655
[14, 300] loss: 0.687
[14, 360] loss: 0.666
Epoch: 14 -> Loss: 0.894064247608
Epoch: 14 -> Test Accuracy: 69.9
[15, 60] loss: 0.645
[15, 120] loss: 0.653
[15, 180] loss: 0.676
[15, 240] loss: 0.659
[15, 300] loss: 0.663
[15, 360] loss: 0.663
Epoch: 15 -> Loss: 0.635680139065
Epoch: 15 -> Test Accuracy: 69.58
[16, 60] loss: 0.651
[16, 120] loss: 0.651
[16, 180] loss: 0.672
[16, 240] loss: 0.658
[16, 300] loss: 0.680
[16, 360] loss: 0.669
Epoch: 16 -> Loss: 0.502421319485
Epoch: 16 -> Test Accuracy: 70.04
[17, 60] loss: 0.660
[17, 120] loss: 0.648
[17, 180] loss: 0.658
[17, 240] loss: 0.648
[17, 300] loss: 0.649
[17, 360] loss: 0.671
Epoch: 17 -> Loss: 0.704840838909
Epoch: 17 -> Test Accuracy: 70.54
[18, 60] loss: 0.644
[18, 120] loss: 0.661
[18, 180] loss: 0.676
[18, 240] loss: 0.688
[18, 300] loss: 0.675
[18, 360] loss: 0.652
Epoch: 18 -> Loss: 0.815052688122
Epoch: 18 -> Test Accuracy: 70.73
[19, 60] loss: 0.649
[19, 120] loss: 0.635
[19, 180] loss: 0.679
[19, 240] loss: 0.674
[19, 300] loss: 0.653
[19, 360] loss: 0.660
Epoch: 19 -> Loss: 0.764343142509
Epoch: 19 -> Test Accuracy: 70.64
[20, 60] loss: 0.663
[20, 120] loss: 0.650
[20, 180] loss: 0.639
[20, 240] loss: 0.665
[20, 300] loss: 0.666
[20, 360] loss: 0.654
Epoch: 20 -> Loss: 0.586040139198
Epoch: 20 -> Test Accuracy: 69.56
[21, 60] loss: 0.656
[21, 120] loss: 0.645
[21, 180] loss: 0.643
[21, 240] loss: 0.676
[21, 300] loss: 0.637
[21, 360] loss: 0.654
Epoch: 21 -> Loss: 0.644037604332
Epoch: 21 -> Test Accuracy: 71.2
[22, 60] loss: 0.651
[22, 120] loss: 0.657
[22, 180] loss: 0.653
[22, 240] loss: 0.647
[22, 300] loss: 0.653
[22, 360] loss: 0.662
Epoch: 22 -> Loss: 0.812040984631
Epoch: 22 -> Test Accuracy: 71.79
[23, 60] loss: 0.658
[23, 120] loss: 0.624
[23, 180] loss: 0.651
[23, 240] loss: 0.638
[23, 300] loss: 0.644
[23, 360] loss: 0.677
Epoch: 23 -> Loss: 0.578508019447
Epoch: 23 -> Test Accuracy: 71.05
[24, 60] loss: 0.635
[24, 120] loss: 0.646
[24, 180] loss: 0.664
[24, 240] loss: 0.664
[24, 300] loss: 0.655
[24, 360] loss: 0.666
Epoch: 24 -> Loss: 0.662880301476
Epoch: 24 -> Test Accuracy: 71.53
[25, 60] loss: 0.630
[25, 120] loss: 0.651
[25, 180] loss: 0.647
[25, 240] loss: 0.658
[25, 300] loss: 0.638
[25, 360] loss: 0.661
Epoch: 25 -> Loss: 0.739185631275
Epoch: 25 -> Test Accuracy: 70.33
[26, 60] loss: 0.669
[26, 120] loss: 0.654
[26, 180] loss: 0.658
[26, 240] loss: 0.659
[26, 300] loss: 0.636
[26, 360] loss: 0.661
Epoch: 26 -> Loss: 0.637153983116
Epoch: 26 -> Test Accuracy: 70.77
[27, 60] loss: 0.633
[27, 120] loss: 0.658
[27, 180] loss: 0.650
[27, 240] loss: 0.650
[27, 300] loss: 0.646
[27, 360] loss: 0.659
Epoch: 27 -> Loss: 0.662607073784
Epoch: 27 -> Test Accuracy: 71.39
[28, 60] loss: 0.655
[28, 120] loss: 0.652
[28, 180] loss: 0.639
[28, 240] loss: 0.645
[28, 300] loss: 0.654
[28, 360] loss: 0.661
Epoch: 28 -> Loss: 0.467119842768
Epoch: 28 -> Test Accuracy: 72.14
[29, 60] loss: 0.635
[29, 120] loss: 0.631
[29, 180] loss: 0.654
[29, 240] loss: 0.666
[29, 300] loss: 0.639
[29, 360] loss: 0.650
Epoch: 29 -> Loss: 0.704536378384
Epoch: 29 -> Test Accuracy: 69.67
[30, 60] loss: 0.662
[30, 120] loss: 0.636
[30, 180] loss: 0.637
[30, 240] loss: 0.650
[30, 300] loss: 0.651
[30, 360] loss: 0.668
Epoch: 30 -> Loss: 0.742483079433
Epoch: 30 -> Test Accuracy: 71.63
[31, 60] loss: 0.655
[31, 120] loss: 0.605
[31, 180] loss: 0.647
[31, 240] loss: 0.679
[31, 300] loss: 0.639
[31, 360] loss: 0.677
Epoch: 31 -> Loss: 0.812393546104
Epoch: 31 -> Test Accuracy: 70.79
[32, 60] loss: 0.630
[32, 120] loss: 0.649
[32, 180] loss: 0.675
[32, 240] loss: 0.629
[32, 300] loss: 0.654
[32, 360] loss: 0.659
Epoch: 32 -> Loss: 0.834937930107
Epoch: 32 -> Test Accuracy: 70.19
[33, 60] loss: 0.632
[33, 120] loss: 0.665
[33, 180] loss: 0.652
[33, 240] loss: 0.645
[33, 300] loss: 0.647
[33, 360] loss: 0.658
Epoch: 33 -> Loss: 0.631586313248
Epoch: 33 -> Test Accuracy: 71.43
[34, 60] loss: 0.632
[34, 120] loss: 0.657
[34, 180] loss: 0.656
[34, 240] loss: 0.646
[34, 300] loss: 0.636
[34, 360] loss: 0.644
Epoch: 34 -> Loss: 0.700482487679
Epoch: 34 -> Test Accuracy: 70.64
[35, 60] loss: 0.639
[35, 120] loss: 0.652
[35, 180] loss: 0.658
[35, 240] loss: 0.633
[35, 300] loss: 0.635
[35, 360] loss: 0.643
Epoch: 35 -> Loss: 0.765577435493
Epoch: 35 -> Test Accuracy: 70.54
[36, 60] loss: 0.598
[36, 120] loss: 0.574
[36, 180] loss: 0.555
[36, 240] loss: 0.571
[36, 300] loss: 0.544
[36, 360] loss: 0.560
Epoch: 36 -> Loss: 0.447709709406
Epoch: 36 -> Test Accuracy: 74.07
[37, 60] loss: 0.555
[37, 120] loss: 0.527
[37, 180] loss: 0.550
[37, 240] loss: 0.546
[37, 300] loss: 0.532
[37, 360] loss: 0.538
Epoch: 37 -> Loss: 0.494886398315
Epoch: 37 -> Test Accuracy: 74.87
[38, 60] loss: 0.531
[38, 120] loss: 0.521
[38, 180] loss: 0.538
[38, 240] loss: 0.539
[38, 300] loss: 0.534
[38, 360] loss: 0.549
Epoch: 38 -> Loss: 0.617129087448
Epoch: 38 -> Test Accuracy: 74.88
[39, 60] loss: 0.527
[39, 120] loss: 0.535
[39, 180] loss: 0.503
[39, 240] loss: 0.530
[39, 300] loss: 0.529
[39, 360] loss: 0.533
Epoch: 39 -> Loss: 0.571642994881
Epoch: 39 -> Test Accuracy: 74.93
[40, 60] loss: 0.505
[40, 120] loss: 0.526
[40, 180] loss: 0.524
[40, 240] loss: 0.539
[40, 300] loss: 0.523
[40, 360] loss: 0.529
Epoch: 40 -> Loss: 0.464881032705
Epoch: 40 -> Test Accuracy: 74.44
[41, 60] loss: 0.526
[41, 120] loss: 0.514
[41, 180] loss: 0.512
[41, 240] loss: 0.526
[41, 300] loss: 0.508
[41, 360] loss: 0.529
Epoch: 41 -> Loss: 0.545656323433
Epoch: 41 -> Test Accuracy: 75.06
[42, 60] loss: 0.517
[42, 120] loss: 0.541
[42, 180] loss: 0.517
[42, 240] loss: 0.511
[42, 300] loss: 0.530
[42, 360] loss: 0.529
Epoch: 42 -> Loss: 0.519388616085
Epoch: 42 -> Test Accuracy: 74.73
[43, 60] loss: 0.511
[43, 120] loss: 0.527
[43, 180] loss: 0.495
[43, 240] loss: 0.513
[43, 300] loss: 0.539
[43, 360] loss: 0.530
Epoch: 43 -> Loss: 0.384705603123
Epoch: 43 -> Test Accuracy: 74.91
[44, 60] loss: 0.510
[44, 120] loss: 0.501
[44, 180] loss: 0.527
[44, 240] loss: 0.525
[44, 300] loss: 0.526
[44, 360] loss: 0.534
Epoch: 44 -> Loss: 0.535769581795
Epoch: 44 -> Test Accuracy: 74.7
[45, 60] loss: 0.512
[45, 120] loss: 0.514
[45, 180] loss: 0.518
[45, 240] loss: 0.537
[45, 300] loss: 0.518
[45, 360] loss: 0.531
Epoch: 45 -> Loss: 0.515571177006
Epoch: 45 -> Test Accuracy: 74.67
[46, 60] loss: 0.508
[46, 120] loss: 0.496
[46, 180] loss: 0.519
[46, 240] loss: 0.521
[46, 300] loss: 0.527
[46, 360] loss: 0.548
Epoch: 46 -> Loss: 0.589191794395
Epoch: 46 -> Test Accuracy: 74.65
[47, 60] loss: 0.512
[47, 120] loss: 0.509
[47, 180] loss: 0.522
[47, 240] loss: 0.511
[47, 300] loss: 0.526
[47, 360] loss: 0.536
Epoch: 47 -> Loss: 0.578607082367
Epoch: 47 -> Test Accuracy: 74.46
[48, 60] loss: 0.521
[48, 120] loss: 0.514
[48, 180] loss: 0.534
[48, 240] loss: 0.491
[48, 300] loss: 0.517
[48, 360] loss: 0.530
Epoch: 48 -> Loss: 0.517775058746
Epoch: 48 -> Test Accuracy: 74.73
[49, 60] loss: 0.505
[49, 120] loss: 0.512
[49, 180] loss: 0.503
[49, 240] loss: 0.529
[49, 300] loss: 0.526
[49, 360] loss: 0.511
Epoch: 49 -> Loss: 0.652405083179
Epoch: 49 -> Test Accuracy: 73.27
[50, 60] loss: 0.507
[50, 120] loss: 0.517
[50, 180] loss: 0.520
[50, 240] loss: 0.529
[50, 300] loss: 0.529
[50, 360] loss: 0.522
Epoch: 50 -> Loss: 0.509332418442
Epoch: 50 -> Test Accuracy: 74.12
[51, 60] loss: 0.525
[51, 120] loss: 0.522
[51, 180] loss: 0.502
[51, 240] loss: 0.521
[51, 300] loss: 0.523
[51, 360] loss: 0.530
Epoch: 51 -> Loss: 0.664568245411
Epoch: 51 -> Test Accuracy: 73.92
[52, 60] loss: 0.502
[52, 120] loss: 0.504
[52, 180] loss: 0.503
[52, 240] loss: 0.524
[52, 300] loss: 0.538
[52, 360] loss: 0.502
Epoch: 52 -> Loss: 0.807490944862
Epoch: 52 -> Test Accuracy: 74.28
[53, 60] loss: 0.506
[53, 120] loss: 0.504
[53, 180] loss: 0.532
[53, 240] loss: 0.544
[53, 300] loss: 0.526
[53, 360] loss: 0.513
Epoch: 53 -> Loss: 0.50402867794
Epoch: 53 -> Test Accuracy: 74.45
[54, 60] loss: 0.510
[54, 120] loss: 0.498
[54, 180] loss: 0.525
[54, 240] loss: 0.520
[54, 300] loss: 0.522
[54, 360] loss: 0.513
Epoch: 54 -> Loss: 0.543969511986
Epoch: 54 -> Test Accuracy: 74.06
[55, 60] loss: 0.516
[55, 120] loss: 0.514
[55, 180] loss: 0.541
[55, 240] loss: 0.508
[55, 300] loss: 0.524
[55, 360] loss: 0.534
Epoch: 55 -> Loss: 0.437804788351
Epoch: 55 -> Test Accuracy: 74.47
[56, 60] loss: 0.510
[56, 120] loss: 0.505
[56, 180] loss: 0.513
[56, 240] loss: 0.513
[56, 300] loss: 0.513
[56, 360] loss: 0.529
Epoch: 56 -> Loss: 0.575148999691
Epoch: 56 -> Test Accuracy: 74.21
[57, 60] loss: 0.505
[57, 120] loss: 0.515
[57, 180] loss: 0.527
[57, 240] loss: 0.499
[57, 300] loss: 0.516
[57, 360] loss: 0.529
Epoch: 57 -> Loss: 0.565602242947
Epoch: 57 -> Test Accuracy: 74.27
[58, 60] loss: 0.508
[58, 120] loss: 0.498
[58, 180] loss: 0.513
[58, 240] loss: 0.524
[58, 300] loss: 0.529
[58, 360] loss: 0.522
Epoch: 58 -> Loss: 0.326432406902
Epoch: 58 -> Test Accuracy: 74.3
[59, 60] loss: 0.490
[59, 120] loss: 0.527
[59, 180] loss: 0.512
[59, 240] loss: 0.501
[59, 300] loss: 0.526
[59, 360] loss: 0.512
Epoch: 59 -> Loss: 0.586781084538
Epoch: 59 -> Test Accuracy: 74.1
[60, 60] loss: 0.513
[60, 120] loss: 0.529
[60, 180] loss: 0.522
[60, 240] loss: 0.516
[60, 300] loss: 0.524
[60, 360] loss: 0.517
Epoch: 60 -> Loss: 0.510123133659
Epoch: 60 -> Test Accuracy: 74.74
[61, 60] loss: 0.502
[61, 120] loss: 0.503
[61, 180] loss: 0.502
[61, 240] loss: 0.528
[61, 300] loss: 0.507
[61, 360] loss: 0.510
Epoch: 61 -> Loss: 0.695623755455
Epoch: 61 -> Test Accuracy: 74.29
[62, 60] loss: 0.520
[62, 120] loss: 0.508
[62, 180] loss: 0.500
[62, 240] loss: 0.528
[62, 300] loss: 0.488
[62, 360] loss: 0.528
Epoch: 62 -> Loss: 0.405204713345
Epoch: 62 -> Test Accuracy: 74.26
[63, 60] loss: 0.507
[63, 120] loss: 0.519
[63, 180] loss: 0.518
[63, 240] loss: 0.499
[63, 300] loss: 0.488
[63, 360] loss: 0.515
Epoch: 63 -> Loss: 0.57899081707
Epoch: 63 -> Test Accuracy: 74.48
[64, 60] loss: 0.482
[64, 120] loss: 0.515
[64, 180] loss: 0.505
[64, 240] loss: 0.511
[64, 300] loss: 0.518
[64, 360] loss: 0.519
Epoch: 64 -> Loss: 0.512594163418
Epoch: 64 -> Test Accuracy: 74.79
[65, 60] loss: 0.497
[65, 120] loss: 0.500
[65, 180] loss: 0.516
[65, 240] loss: 0.497
[65, 300] loss: 0.523
[65, 360] loss: 0.525
Epoch: 65 -> Loss: 0.457039773464
Epoch: 65 -> Test Accuracy: 74.52
[66, 60] loss: 0.512
[66, 120] loss: 0.508
[66, 180] loss: 0.500
[66, 240] loss: 0.509
[66, 300] loss: 0.511
[66, 360] loss: 0.513
Epoch: 66 -> Loss: 0.532711863518
Epoch: 66 -> Test Accuracy: 74.92
[67, 60] loss: 0.491
[67, 120] loss: 0.502
[67, 180] loss: 0.518
[67, 240] loss: 0.533
[67, 300] loss: 0.503
[67, 360] loss: 0.503
Epoch: 67 -> Loss: 0.493550598621
Epoch: 67 -> Test Accuracy: 74.01
[68, 60] loss: 0.505
[68, 120] loss: 0.493
[68, 180] loss: 0.529
[68, 240] loss: 0.501
[68, 300] loss: 0.504
[68, 360] loss: 0.534
Epoch: 68 -> Loss: 0.395356237888
Epoch: 68 -> Test Accuracy: 73.78
[69, 60] loss: 0.495
[69, 120] loss: 0.506
[69, 180] loss: 0.498
[69, 240] loss: 0.506
[69, 300] loss: 0.522
[69, 360] loss: 0.502
Epoch: 69 -> Loss: 0.556714773178
Epoch: 69 -> Test Accuracy: 73.74
[70, 60] loss: 0.503
[70, 120] loss: 0.500
[70, 180] loss: 0.513
[70, 240] loss: 0.516
[70, 300] loss: 0.500
[70, 360] loss: 0.507
Epoch: 70 -> Loss: 0.507016658783
Epoch: 70 -> Test Accuracy: 74.49
[71, 60] loss: 0.471
[71, 120] loss: 0.452
[71, 180] loss: 0.427
[71, 240] loss: 0.449
[71, 300] loss: 0.440
[71, 360] loss: 0.470
Epoch: 71 -> Loss: 0.573578238487
Epoch: 71 -> Test Accuracy: 76.35
[72, 60] loss: 0.448
[72, 120] loss: 0.426
[72, 180] loss: 0.451
[72, 240] loss: 0.441
[72, 300] loss: 0.438
[72, 360] loss: 0.428
Epoch: 72 -> Loss: 0.490967571735
Epoch: 72 -> Test Accuracy: 76.48
[73, 60] loss: 0.424
[73, 120] loss: 0.436
[73, 180] loss: 0.429
[73, 240] loss: 0.442
[73, 300] loss: 0.442
[73, 360] loss: 0.426
Epoch: 73 -> Loss: 0.503980875015
Epoch: 73 -> Test Accuracy: 76.69
[74, 60] loss: 0.429
[74, 120] loss: 0.425
[74, 180] loss: 0.429
[74, 240] loss: 0.423
[74, 300] loss: 0.422
[74, 360] loss: 0.415
Epoch: 74 -> Loss: 0.602517187595
Epoch: 74 -> Test Accuracy: 76.84
[75, 60] loss: 0.409
[75, 120] loss: 0.426
[75, 180] loss: 0.433
[75, 240] loss: 0.426
[75, 300] loss: 0.414
[75, 360] loss: 0.439
Epoch: 75 -> Loss: 0.393859446049
Epoch: 75 -> Test Accuracy: 76.43
[76, 60] loss: 0.413
[76, 120] loss: 0.426
[76, 180] loss: 0.425
[76, 240] loss: 0.430
[76, 300] loss: 0.418
[76, 360] loss: 0.417
Epoch: 76 -> Loss: 0.406775295734
Epoch: 76 -> Test Accuracy: 76.52
[77, 60] loss: 0.404
[77, 120] loss: 0.419
[77, 180] loss: 0.424
[77, 240] loss: 0.421
[77, 300] loss: 0.423
[77, 360] loss: 0.422
Epoch: 77 -> Loss: 0.478052318096
Epoch: 77 -> Test Accuracy: 76.52
[78, 60] loss: 0.413
[78, 120] loss: 0.420
[78, 180] loss: 0.412
[78, 240] loss: 0.411
[78, 300] loss: 0.409
[78, 360] loss: 0.429
Epoch: 78 -> Loss: 0.368268817663
Epoch: 78 -> Test Accuracy: 76.57
[79, 60] loss: 0.419
[79, 120] loss: 0.411
[79, 180] loss: 0.408
[79, 240] loss: 0.428
[79, 300] loss: 0.416
[79, 360] loss: 0.409
Epoch: 79 -> Loss: 0.373751223087
Epoch: 79 -> Test Accuracy: 76.82
[80, 60] loss: 0.390
[80, 120] loss: 0.410
[80, 180] loss: 0.406
[80, 240] loss: 0.410
[80, 300] loss: 0.415
[80, 360] loss: 0.420
Epoch: 80 -> Loss: 0.37625131011
Epoch: 80 -> Test Accuracy: 76.64
[81, 60] loss: 0.407
[81, 120] loss: 0.418
[81, 180] loss: 0.409
[81, 240] loss: 0.402
[81, 300] loss: 0.420
[81, 360] loss: 0.418
Epoch: 81 -> Loss: 0.494882762432
Epoch: 81 -> Test Accuracy: 76.25
[82, 60] loss: 0.410
[82, 120] loss: 0.412
[82, 180] loss: 0.407
[82, 240] loss: 0.413
[82, 300] loss: 0.416
[82, 360] loss: 0.402
Epoch: 82 -> Loss: 0.318713784218
Epoch: 82 -> Test Accuracy: 76.53
[83, 60] loss: 0.402
[83, 120] loss: 0.408
[83, 180] loss: 0.402
[83, 240] loss: 0.418
[83, 300] loss: 0.404
[83, 360] loss: 0.415
Epoch: 83 -> Loss: 0.441703498363
Epoch: 83 -> Test Accuracy: 76.49
[84, 60] loss: 0.410
[84, 120] loss: 0.409
[84, 180] loss: 0.395
[84, 240] loss: 0.411
[84, 300] loss: 0.409
[84, 360] loss: 0.405
Epoch: 84 -> Loss: 0.360123574734
Epoch: 84 -> Test Accuracy: 76.15
[85, 60] loss: 0.398
[85, 120] loss: 0.408
[85, 180] loss: 0.405
[85, 240] loss: 0.406
[85, 300] loss: 0.410
[85, 360] loss: 0.420
Epoch: 85 -> Loss: 0.449885308743
Epoch: 85 -> Test Accuracy: 76.36
[86, 60] loss: 0.393
[86, 120] loss: 0.395
[86, 180] loss: 0.381
[86, 240] loss: 0.378
[86, 300] loss: 0.375
[86, 360] loss: 0.394
Epoch: 86 -> Loss: 0.360383838415
Epoch: 86 -> Test Accuracy: 76.94
[87, 60] loss: 0.383
[87, 120] loss: 0.393
[87, 180] loss: 0.376
[87, 240] loss: 0.378
[87, 300] loss: 0.367
[87, 360] loss: 0.393
Epoch: 87 -> Loss: 0.313582003117
Epoch: 87 -> Test Accuracy: 76.93
[88, 60] loss: 0.375
[88, 120] loss: 0.373
[88, 180] loss: 0.389
[88, 240] loss: 0.381
[88, 300] loss: 0.376
[88, 360] loss: 0.380
Epoch: 88 -> Loss: 0.419721186161
Epoch: 88 -> Test Accuracy: 77.0
[89, 60] loss: 0.373
[89, 120] loss: 0.379
[89, 180] loss: 0.373
[89, 240] loss: 0.369
[89, 300] loss: 0.378
[89, 360] loss: 0.390
Epoch: 89 -> Loss: 0.305138349533
Epoch: 89 -> Test Accuracy: 77.05
[90, 60] loss: 0.376
[90, 120] loss: 0.388
[90, 180] loss: 0.370
[90, 240] loss: 0.382
[90, 300] loss: 0.379
[90, 360] loss: 0.375
Epoch: 90 -> Loss: 0.428806781769
Epoch: 90 -> Test Accuracy: 77.04
[91, 60] loss: 0.382
[91, 120] loss: 0.373
[91, 180] loss: 0.379
[91, 240] loss: 0.371
[91, 300] loss: 0.366
[91, 360] loss: 0.378
Epoch: 91 -> Loss: 0.242852404714
Epoch: 91 -> Test Accuracy: 77.2
[92, 60] loss: 0.377
[92, 120] loss: 0.363
[92, 180] loss: 0.376
[92, 240] loss: 0.370
[92, 300] loss: 0.383
[92, 360] loss: 0.380
Epoch: 92 -> Loss: 0.294145166874
Epoch: 92 -> Test Accuracy: 76.88
[93, 60] loss: 0.366
[93, 120] loss: 0.387
[93, 180] loss: 0.376
[93, 240] loss: 0.371
[93, 300] loss: 0.361
[93, 360] loss: 0.376
Epoch: 93 -> Loss: 0.481191962957
Epoch: 93 -> Test Accuracy: 77.21
[94, 60] loss: 0.363
[94, 120] loss: 0.375
[94, 180] loss: 0.370
[94, 240] loss: 0.375
[94, 300] loss: 0.385
[94, 360] loss: 0.381
Epoch: 94 -> Loss: 0.371431410313
Epoch: 94 -> Test Accuracy: 77.03
[95, 60] loss: 0.373
[95, 120] loss: 0.383
[95, 180] loss: 0.377
[95, 240] loss: 0.358
[95, 300] loss: 0.378
[95, 360] loss: 0.379
Epoch: 95 -> Loss: 0.443098962307
Epoch: 95 -> Test Accuracy: 77.05
[96, 60] loss: 0.386
[96, 120] loss: 0.369
[96, 180] loss: 0.375
[96, 240] loss: 0.371
[96, 300] loss: 0.376
[96, 360] loss: 0.370
Epoch: 96 -> Loss: 0.290161937475
Epoch: 96 -> Test Accuracy: 77.3
[97, 60] loss: 0.364
[97, 120] loss: 0.369
[97, 180] loss: 0.373
[97, 240] loss: 0.371
[97, 300] loss: 0.366
[97, 360] loss: 0.369
Epoch: 97 -> Loss: 0.397721797228
Epoch: 97 -> Test Accuracy: 77.16
[98, 60] loss: 0.369
[98, 120] loss: 0.366
[98, 180] loss: 0.364
[98, 240] loss: 0.367
[98, 300] loss: 0.370
[98, 360] loss: 0.371
Epoch: 98 -> Loss: 0.407518237829
Epoch: 98 -> Test Accuracy: 77.12
[99, 60] loss: 0.363
[99, 120] loss: 0.376
[99, 180] loss: 0.368
[99, 240] loss: 0.350
[99, 300] loss: 0.369
[99, 360] loss: 0.369
Epoch: 99 -> Loss: 0.345765531063
Epoch: 99 -> Test Accuracy: 76.94
[100, 60] loss: 0.366
[100, 120] loss: 0.351
[100, 180] loss: 0.393
[100, 240] loss: 0.368
[100, 300] loss: 0.362
[100, 360] loss: 0.379
Epoch: 100 -> Loss: 0.41921916604
Epoch: 100 -> Test Accuracy: 76.96
Finished Training
[1, 60] loss: 2.214
[1, 120] loss: 2.042
[1, 180] loss: 1.986
[1, 240] loss: 1.957
[1, 300] loss: 1.930
[1, 360] loss: 1.906
Epoch: 1 -> Loss: 1.80112838745
Epoch: 1 -> Test Accuracy: 28.92
[2, 60] loss: 1.875
[2, 120] loss: 1.874
[2, 180] loss: 1.869
[2, 240] loss: 1.861
[2, 300] loss: 1.843
[2, 360] loss: 1.829
Epoch: 2 -> Loss: 1.84579432011
Epoch: 2 -> Test Accuracy: 29.05
[3, 60] loss: 1.825
[3, 120] loss: 1.802
[3, 180] loss: 1.822
[3, 240] loss: 1.818
[3, 300] loss: 1.822
[3, 360] loss: 1.799
Epoch: 3 -> Loss: 1.66067254543
Epoch: 3 -> Test Accuracy: 32.7
[4, 60] loss: 1.771
[4, 120] loss: 1.790
[4, 180] loss: 1.793
[4, 240] loss: 1.789
[4, 300] loss: 1.766
[4, 360] loss: 1.767
Epoch: 4 -> Loss: 1.83661556244
Epoch: 4 -> Test Accuracy: 31.34
[5, 60] loss: 1.773
[5, 120] loss: 1.760
[5, 180] loss: 1.760
[5, 240] loss: 1.758
[5, 300] loss: 1.768
[5, 360] loss: 1.770
Epoch: 5 -> Loss: 1.5981400013
Epoch: 5 -> Test Accuracy: 32.06
[6, 60] loss: 1.739
[6, 120] loss: 1.751
[6, 180] loss: 1.744
[6, 240] loss: 1.743
[6, 300] loss: 1.751
[6, 360] loss: 1.740
Epoch: 6 -> Loss: 1.93392252922
Epoch: 6 -> Test Accuracy: 32.84
[7, 60] loss: 1.764
[7, 120] loss: 1.720
[7, 180] loss: 1.725
[7, 240] loss: 1.734
[7, 300] loss: 1.728
[7, 360] loss: 1.743
Epoch: 7 -> Loss: 1.77970945835
Epoch: 7 -> Test Accuracy: 33.46
[8, 60] loss: 1.727
[8, 120] loss: 1.729
[8, 180] loss: 1.729
[8, 240] loss: 1.739
[8, 300] loss: 1.732
[8, 360] loss: 1.735
Epoch: 8 -> Loss: 1.62089002132
Epoch: 8 -> Test Accuracy: 32.72
[9, 60] loss: 1.725
[9, 120] loss: 1.718
[9, 180] loss: 1.719
[9, 240] loss: 1.720
[9, 300] loss: 1.718
[9, 360] loss: 1.725
Epoch: 9 -> Loss: 1.67075884342
Epoch: 9 -> Test Accuracy: 33.08
[10, 60] loss: 1.720
[10, 120] loss: 1.714
[10, 180] loss: 1.709
[10, 240] loss: 1.728
[10, 300] loss: 1.739
[10, 360] loss: 1.712
Epoch: 10 -> Loss: 1.62930130959
Epoch: 10 -> Test Accuracy: 32.78
[11, 60] loss: 1.709
[11, 120] loss: 1.707
[11, 180] loss: 1.702
[11, 240] loss: 1.704
[11, 300] loss: 1.705
[11, 360] loss: 1.709
Epoch: 11 -> Loss: 1.67310643196
Epoch: 11 -> Test Accuracy: 33.96
[12, 60] loss: 1.696
[12, 120] loss: 1.705
[12, 180] loss: 1.714
[12, 240] loss: 1.717
[12, 300] loss: 1.708
[12, 360] loss: 1.715
Epoch: 12 -> Loss: 1.63214170933
Epoch: 12 -> Test Accuracy: 34.25
[13, 60] loss: 1.707
[13, 120] loss: 1.684
[13, 180] loss: 1.707
[13, 240] loss: 1.698
[13, 300] loss: 1.696
[13, 360] loss: 1.704
Epoch: 13 -> Loss: 1.7707092762
Epoch: 13 -> Test Accuracy: 33.62
[14, 60] loss: 1.684
[14, 120] loss: 1.690
[14, 180] loss: 1.704
[14, 240] loss: 1.686
[14, 300] loss: 1.689
[14, 360] loss: 1.715
Epoch: 14 -> Loss: 1.63841688633
Epoch: 14 -> Test Accuracy: 34.7
[15, 60] loss: 1.699
[15, 120] loss: 1.684
[15, 180] loss: 1.696
[15, 240] loss: 1.691
[15, 300] loss: 1.684
[15, 360] loss: 1.699
Epoch: 15 -> Loss: 1.84138941765
Epoch: 15 -> Test Accuracy: 35.28
[16, 60] loss: 1.690
[16, 120] loss: 1.697
[16, 180] loss: 1.693
[16, 240] loss: 1.677
[16, 300] loss: 1.678
[16, 360] loss: 1.698
Epoch: 16 -> Loss: 1.51885926723
Epoch: 16 -> Test Accuracy: 35.16
[17, 60] loss: 1.692
[17, 120] loss: 1.682
[17, 180] loss: 1.704
[17, 240] loss: 1.695
[17, 300] loss: 1.677
[17, 360] loss: 1.686
Epoch: 17 -> Loss: 1.63488316536
Epoch: 17 -> Test Accuracy: 35.33
[18, 60] loss: 1.690
[18, 120] loss: 1.679
[18, 180] loss: 1.679
[18, 240] loss: 1.661
[18, 300] loss: 1.699
[18, 360] loss: 1.670
Epoch: 18 -> Loss: 1.67851006985
Epoch: 18 -> Test Accuracy: 33.34
[19, 60] loss: 1.669
[19, 120] loss: 1.687
[19, 180] loss: 1.684
[19, 240] loss: 1.695
[19, 300] loss: 1.680
[19, 360] loss: 1.696
Epoch: 19 -> Loss: 1.74293398857
Epoch: 19 -> Test Accuracy: 35.16
[20, 60] loss: 1.683
[20, 120] loss: 1.684
[20, 180] loss: 1.659
[20, 240] loss: 1.677
[20, 300] loss: 1.680
[20, 360] loss: 1.667
Epoch: 20 -> Loss: 1.69578421116
Epoch: 20 -> Test Accuracy: 34.05
[21, 60] loss: 1.661
[21, 120] loss: 1.689
[21, 180] loss: 1.681
[21, 240] loss: 1.683
[21, 300] loss: 1.697
[21, 360] loss: 1.681
Epoch: 21 -> Loss: 1.69357419014
Epoch: 21 -> Test Accuracy: 34.2
[22, 60] loss: 1.672
[22, 120] loss: 1.656
[22, 180] loss: 1.684
[22, 240] loss: 1.688
[22, 300] loss: 1.680
[22, 360] loss: 1.678
Epoch: 22 -> Loss: 1.80121481419
Epoch: 22 -> Test Accuracy: 34.95
[23, 60] loss: 1.664
[23, 120] loss: 1.688
[23, 180] loss: 1.693
[23, 240] loss: 1.673
[23, 300] loss: 1.682
[23, 360] loss: 1.679
Epoch: 23 -> Loss: 1.7194082737
Epoch: 23 -> Test Accuracy: 33.7
[24, 60] loss: 1.671
[24, 120] loss: 1.674
[24, 180] loss: 1.684
[24, 240] loss: 1.683
[24, 300] loss: 1.679
[24, 360] loss: 1.680
Epoch: 24 -> Loss: 1.72372436523
Epoch: 24 -> Test Accuracy: 33.45
[25, 60] loss: 1.678
[25, 120] loss: 1.673
[25, 180] loss: 1.665
[25, 240] loss: 1.674
[25, 300] loss: 1.673
[25, 360] loss: 1.665
Epoch: 25 -> Loss: 1.63593745232
Epoch: 25 -> Test Accuracy: 35.31
[26, 60] loss: 1.671
[26, 120] loss: 1.665
[26, 180] loss: 1.680
[26, 240] loss: 1.693
[26, 300] loss: 1.667
[26, 360] loss: 1.684
Epoch: 26 -> Loss: 1.64690184593
Epoch: 26 -> Test Accuracy: 35.33
[27, 60] loss: 1.663
[27, 120] loss: 1.686
[27, 180] loss: 1.682
[27, 240] loss: 1.666
[27, 300] loss: 1.658
[27, 360] loss: 1.670
Epoch: 27 -> Loss: 1.75495302677
Epoch: 27 -> Test Accuracy: 34.42
[28, 60] loss: 1.670
[28, 120] loss: 1.673
[28, 180] loss: 1.664
[28, 240] loss: 1.670
[28, 300] loss: 1.683
[28, 360] loss: 1.663
Epoch: 28 -> Loss: 1.61920201778
Epoch: 28 -> Test Accuracy: 35.55
[29, 60] loss: 1.672
[29, 120] loss: 1.664
[29, 180] loss: 1.645
[29, 240] loss: 1.683
[29, 300] loss: 1.665
[29, 360] loss: 1.664
Epoch: 29 -> Loss: 1.5431599617
Epoch: 29 -> Test Accuracy: 36.01
[30, 60] loss: 1.660
[30, 120] loss: 1.649
[30, 180] loss: 1.671
[30, 240] loss: 1.682
[30, 300] loss: 1.666
[30, 360] loss: 1.677
Epoch: 30 -> Loss: 1.83706569672
Epoch: 30 -> Test Accuracy: 35.54
[31, 60] loss: 1.659
[31, 120] loss: 1.652
[31, 180] loss: 1.675
[31, 240] loss: 1.680
[31, 300] loss: 1.661
[31, 360] loss: 1.663
Epoch: 31 -> Loss: 1.68681812286
Epoch: 31 -> Test Accuracy: 35.16
[32, 60] loss: 1.667
[32, 120] loss: 1.669
[32, 180] loss: 1.656
[32, 240] loss: 1.661
[32, 300] loss: 1.660
[32, 360] loss: 1.663
Epoch: 32 -> Loss: 1.65721535683
Epoch: 32 -> Test Accuracy: 35.56
[33, 60] loss: 1.656
[33, 120] loss: 1.682
[33, 180] loss: 1.672
[33, 240] loss: 1.660
[33, 300] loss: 1.668
[33, 360] loss: 1.648
Epoch: 33 -> Loss: 1.63733065128
Epoch: 33 -> Test Accuracy: 36.06
[34, 60] loss: 1.672
[34, 120] loss: 1.657
[34, 180] loss: 1.668
[34, 240] loss: 1.661
[34, 300] loss: 1.662
[34, 360] loss: 1.672
Epoch: 34 -> Loss: 1.68781399727
Epoch: 34 -> Test Accuracy: 35.21
[35, 60] loss: 1.650
[35, 120] loss: 1.643
[35, 180] loss: 1.669
[35, 240] loss: 1.657
[35, 300] loss: 1.663
[35, 360] loss: 1.669
Epoch: 35 -> Loss: 1.5745254755
Epoch: 35 -> Test Accuracy: 34.66
[36, 60] loss: 1.620
[36, 120] loss: 1.562
[36, 180] loss: 1.584
[36, 240] loss: 1.564
[36, 300] loss: 1.567
[36, 360] loss: 1.548
Epoch: 36 -> Loss: 1.50426459312
Epoch: 36 -> Test Accuracy: 39.11
[37, 60] loss: 1.558
[37, 120] loss: 1.552
[37, 180] loss: 1.524
[37, 240] loss: 1.545
[37, 300] loss: 1.557
[37, 360] loss: 1.552
Epoch: 37 -> Loss: 1.49290013313
Epoch: 37 -> Test Accuracy: 39.13
[38, 60] loss: 1.556
[38, 120] loss: 1.531
[38, 180] loss: 1.522
[38, 240] loss: 1.554
[38, 300] loss: 1.531
[38, 360] loss: 1.543
Epoch: 38 -> Loss: 1.3990226984
Epoch: 38 -> Test Accuracy: 38.42
[39, 60] loss: 1.527
[39, 120] loss: 1.546
[39, 180] loss: 1.532
[39, 240] loss: 1.543
[39, 300] loss: 1.544
[39, 360] loss: 1.526
Epoch: 39 -> Loss: 1.47474956512
Epoch: 39 -> Test Accuracy: 39.79
[40, 60] loss: 1.526
[40, 120] loss: 1.523
[40, 180] loss: 1.546
[40, 240] loss: 1.530
[40, 300] loss: 1.517
[40, 360] loss: 1.551
Epoch: 40 -> Loss: 1.53031921387
Epoch: 40 -> Test Accuracy: 38.7
[41, 60] loss: 1.540
[41, 120] loss: 1.531
[41, 180] loss: 1.545
[41, 240] loss: 1.530
[41, 300] loss: 1.533
[41, 360] loss: 1.518
Epoch: 41 -> Loss: 1.46427559853
Epoch: 41 -> Test Accuracy: 39.21
[42, 60] loss: 1.521
[42, 120] loss: 1.528
[42, 180] loss: 1.519
[42, 240] loss: 1.527
[42, 300] loss: 1.546
[42, 360] loss: 1.540
Epoch: 42 -> Loss: 1.72309815884
Epoch: 42 -> Test Accuracy: 38.96
[43, 60] loss: 1.534
[43, 120] loss: 1.545
[43, 180] loss: 1.528
[43, 240] loss: 1.529
[43, 300] loss: 1.534
[43, 360] loss: 1.532
Epoch: 43 -> Loss: 1.66689991951
Epoch: 43 -> Test Accuracy: 38.82
[44, 60] loss: 1.529
[44, 120] loss: 1.546
[44, 180] loss: 1.535
[44, 240] loss: 1.507
[44, 300] loss: 1.526
[44, 360] loss: 1.543
Epoch: 44 -> Loss: 1.5062276125
Epoch: 44 -> Test Accuracy: 38.52
[45, 60] loss: 1.530
[45, 120] loss: 1.518
[45, 180] loss: 1.550
[45, 240] loss: 1.532
[45, 300] loss: 1.552
[45, 360] loss: 1.542
Epoch: 45 -> Loss: 1.47612893581
Epoch: 45 -> Test Accuracy: 39.63
[46, 60] loss: 1.515
[46, 120] loss: 1.545
[46, 180] loss: 1.525
[46, 240] loss: 1.533
[46, 300] loss: 1.530
[46, 360] loss: 1.541
Epoch: 46 -> Loss: 1.75978207588
Epoch: 46 -> Test Accuracy: 39.52
[47, 60] loss: 1.524
[47, 120] loss: 1.533
[47, 180] loss: 1.534
[47, 240] loss: 1.552
[47, 300] loss: 1.550
[47, 360] loss: 1.523
Epoch: 47 -> Loss: 1.61171889305
Epoch: 47 -> Test Accuracy: 39.82
[48, 60] loss: 1.527
[48, 120] loss: 1.560
[48, 180] loss: 1.525
[48, 240] loss: 1.541
[48, 300] loss: 1.518
[48, 360] loss: 1.525
Epoch: 48 -> Loss: 1.39809668064
Epoch: 48 -> Test Accuracy: 38.54
[49, 60] loss: 1.521
[49, 120] loss: 1.522
[49, 180] loss: 1.534
[49, 240] loss: 1.530
[49, 300] loss: 1.523
[49, 360] loss: 1.535
Epoch: 49 -> Loss: 1.56974923611
Epoch: 49 -> Test Accuracy: 39.79
[50, 60] loss: 1.515
[50, 120] loss: 1.538
[50, 180] loss: 1.540
[50, 240] loss: 1.515
[50, 300] loss: 1.529
[50, 360] loss: 1.540
Epoch: 50 -> Loss: 1.53327488899
Epoch: 50 -> Test Accuracy: 38.71
[51, 60] loss: 1.525
[51, 120] loss: 1.540
[51, 180] loss: 1.522
[51, 240] loss: 1.542
[51, 300] loss: 1.527
[51, 360] loss: 1.529
Epoch: 51 -> Loss: 1.69156837463
Epoch: 51 -> Test Accuracy: 39.63
[52, 60] loss: 1.511
[52, 120] loss: 1.528
[52, 180] loss: 1.542
[52, 240] loss: 1.522
[52, 300] loss: 1.522
[52, 360] loss: 1.538
Epoch: 52 -> Loss: 1.38336443901
Epoch: 52 -> Test Accuracy: 39.22
[53, 60] loss: 1.530
[53, 120] loss: 1.517
[53, 180] loss: 1.533
[53, 240] loss: 1.524
[53, 300] loss: 1.526
[53, 360] loss: 1.509
Epoch: 53 -> Loss: 1.59752094746
Epoch: 53 -> Test Accuracy: 39.95
[54, 60] loss: 1.533
[54, 120] loss: 1.537
[54, 180] loss: 1.518
[54, 240] loss: 1.544
[54, 300] loss: 1.546
[54, 360] loss: 1.531
Epoch: 54 -> Loss: 1.40547215939
Epoch: 54 -> Test Accuracy: 38.85
[55, 60] loss: 1.525
[55, 120] loss: 1.537
[55, 180] loss: 1.525
[55, 240] loss: 1.515
[55, 300] loss: 1.520
[55, 360] loss: 1.539
Epoch: 55 -> Loss: 1.69401836395
Epoch: 55 -> Test Accuracy: 39.4
[56, 60] loss: 1.503
[56, 120] loss: 1.526
[56, 180] loss: 1.532
[56, 240] loss: 1.537
[56, 300] loss: 1.542
[56, 360] loss: 1.533
Epoch: 56 -> Loss: 1.69933640957
Epoch: 56 -> Test Accuracy: 37.49
[57, 60] loss: 1.534
[57, 120] loss: 1.539
[57, 180] loss: 1.534
[57, 240] loss: 1.530
[57, 300] loss: 1.529
[57, 360] loss: 1.532
Epoch: 57 -> Loss: 1.76949155331
Epoch: 57 -> Test Accuracy: 38.53
[58, 60] loss: 1.514
[58, 120] loss: 1.526
[58, 180] loss: 1.531
[58, 240] loss: 1.512
[58, 300] loss: 1.542
[58, 360] loss: 1.516
Epoch: 58 -> Loss: 1.4495652914
Epoch: 58 -> Test Accuracy: 39.72
[59, 60] loss: 1.521
[59, 120] loss: 1.541
[59, 180] loss: 1.526
[59, 240] loss: 1.526
[59, 300] loss: 1.519
[59, 360] loss: 1.524
Epoch: 59 -> Loss: 1.4615432024
Epoch: 59 -> Test Accuracy: 39.39
[60, 60] loss: 1.542
[60, 120] loss: 1.527
[60, 180] loss: 1.518
[60, 240] loss: 1.515
[60, 300] loss: 1.519
[60, 360] loss: 1.531
Epoch: 60 -> Loss: 1.45971381664
Epoch: 60 -> Test Accuracy: 39.49
[61, 60] loss: 1.521
[61, 120] loss: 1.517
[61, 180] loss: 1.515
[61, 240] loss: 1.509
[61, 300] loss: 1.531
[61, 360] loss: 1.527
Epoch: 61 -> Loss: 1.49451220036
Epoch: 61 -> Test Accuracy: 40.02
[62, 60] loss: 1.508
[62, 120] loss: 1.547
[62, 180] loss: 1.511
[62, 240] loss: 1.516
[62, 300] loss: 1.537
[62, 360] loss: 1.541
Epoch: 62 -> Loss: 1.34865796566
Epoch: 62 -> Test Accuracy: 38.87
[63, 60] loss: 1.545
[63, 120] loss: 1.524
[63, 180] loss: 1.520
[63, 240] loss: 1.530
[63, 300] loss: 1.533
[63, 360] loss: 1.512
Epoch: 63 -> Loss: 1.61831688881
Epoch: 63 -> Test Accuracy: 39.84
[64, 60] loss: 1.525
[64, 120] loss: 1.521
[64, 180] loss: 1.517
[64, 240] loss: 1.531
[64, 300] loss: 1.535
[64, 360] loss: 1.521
Epoch: 64 -> Loss: 1.5459883213
Epoch: 64 -> Test Accuracy: 39.62
[65, 60] loss: 1.520
[65, 120] loss: 1.531
[65, 180] loss: 1.526
[65, 240] loss: 1.501
[65, 300] loss: 1.517
[65, 360] loss: 1.519
Epoch: 65 -> Loss: 1.60374510288
Epoch: 65 -> Test Accuracy: 39.53
[66, 60] loss: 1.515
[66, 120] loss: 1.512
[66, 180] loss: 1.532
[66, 240] loss: 1.512
[66, 300] loss: 1.515
[66, 360] loss: 1.536
Epoch: 66 -> Loss: 1.51004242897
Epoch: 66 -> Test Accuracy: 39.36
[67, 60] loss: 1.514
[67, 120] loss: 1.515
[67, 180] loss: 1.528
[67, 240] loss: 1.521
[67, 300] loss: 1.518
[67, 360] loss: 1.527
Epoch: 67 -> Loss: 1.54676115513
Epoch: 67 -> Test Accuracy: 40.33
[68, 60] loss: 1.520
[68, 120] loss: 1.516
[68, 180] loss: 1.514
[68, 240] loss: 1.521
[68, 300] loss: 1.523
[68, 360] loss: 1.520
Epoch: 68 -> Loss: 1.45840704441
Epoch: 68 -> Test Accuracy: 40.35
[69, 60] loss: 1.508
[69, 120] loss: 1.525
[69, 180] loss: 1.530
[69, 240] loss: 1.525
[69, 300] loss: 1.518
[69, 360] loss: 1.496
Epoch: 69 -> Loss: 1.57058608532
Epoch: 69 -> Test Accuracy: 38.95
[70, 60] loss: 1.514
[70, 120] loss: 1.530
[70, 180] loss: 1.507
[70, 240] loss: 1.520
[70, 300] loss: 1.514
[70, 360] loss: 1.514
Epoch: 70 -> Loss: 1.31466925144
Epoch: 70 -> Test Accuracy: 38.88
[71, 60] loss: 1.489
[71, 120] loss: 1.456
[71, 180] loss: 1.453
[71, 240] loss: 1.453
[71, 300] loss: 1.453
[71, 360] loss: 1.449
Epoch: 71 -> Loss: 1.49236416817
Epoch: 71 -> Test Accuracy: 41.84
[72, 60] loss: 1.437
[72, 120] loss: 1.451
[72, 180] loss: 1.434
[72, 240] loss: 1.430
[72, 300] loss: 1.430
[72, 360] loss: 1.435
Epoch: 72 -> Loss: 1.4060229063
Epoch: 72 -> Test Accuracy: 42.13
[73, 60] loss: 1.443
[73, 120] loss: 1.424
[73, 180] loss: 1.436
[73, 240] loss: 1.435
[73, 300] loss: 1.444
[73, 360] loss: 1.436
Epoch: 73 -> Loss: 1.50561439991
Epoch: 73 -> Test Accuracy: 41.87
[74, 60] loss: 1.421
[74, 120] loss: 1.427
[74, 180] loss: 1.413
[74, 240] loss: 1.438
[74, 300] loss: 1.424
[74, 360] loss: 1.453
Epoch: 74 -> Loss: 1.41287982464
Epoch: 74 -> Test Accuracy: 42.1
[75, 60] loss: 1.424
[75, 120] loss: 1.431
[75, 180] loss: 1.435
[75, 240] loss: 1.432
[75, 300] loss: 1.438
[75, 360] loss: 1.422
Epoch: 75 -> Loss: 1.51846063137
Epoch: 75 -> Test Accuracy: 42.08
[76, 60] loss: 1.435
[76, 120] loss: 1.415
[76, 180] loss: 1.417
[76, 240] loss: 1.423
[76, 300] loss: 1.429
[76, 360] loss: 1.437
Epoch: 76 -> Loss: 1.38681399822
Epoch: 76 -> Test Accuracy: 41.99
[77, 60] loss: 1.432
[77, 120] loss: 1.415
[77, 180] loss: 1.419
[77, 240] loss: 1.412
[77, 300] loss: 1.433
[77, 360] loss: 1.422
Epoch: 77 -> Loss: 1.33687949181
Epoch: 77 -> Test Accuracy: 41.84
[78, 60] loss: 1.425
[78, 120] loss: 1.433
[78, 180] loss: 1.436
[78, 240] loss: 1.439
[78, 300] loss: 1.428
[78, 360] loss: 1.423
Epoch: 78 -> Loss: 1.44703269005
Epoch: 78 -> Test Accuracy: 42.31
[79, 60] loss: 1.423
[79, 120] loss: 1.419
[79, 180] loss: 1.409
[79, 240] loss: 1.442
[79, 300] loss: 1.407
[79, 360] loss: 1.421
Epoch: 79 -> Loss: 1.3942360878
Epoch: 79 -> Test Accuracy: 42.41
[80, 60] loss: 1.423
[80, 120] loss: 1.416
[80, 180] loss: 1.433
[80, 240] loss: 1.409
[80, 300] loss: 1.422
[80, 360] loss: 1.430
Epoch: 80 -> Loss: 1.42237174511
Epoch: 80 -> Test Accuracy: 41.98
[81, 60] loss: 1.421
[81, 120] loss: 1.427
[81, 180] loss: 1.438
[81, 240] loss: 1.431
[81, 300] loss: 1.414
[81, 360] loss: 1.402
Epoch: 81 -> Loss: 1.53787398338
Epoch: 81 -> Test Accuracy: 42.17
[82, 60] loss: 1.411
[82, 120] loss: 1.421
[82, 180] loss: 1.426
[82, 240] loss: 1.409
[82, 300] loss: 1.447
[82, 360] loss: 1.400
Epoch: 82 -> Loss: 1.53660416603
Epoch: 82 -> Test Accuracy: 42.45
[83, 60] loss: 1.420
[83, 120] loss: 1.415
[83, 180] loss: 1.432
[83, 240] loss: 1.410
[83, 300] loss: 1.421
[83, 360] loss: 1.418
Epoch: 83 -> Loss: 1.52950870991
Epoch: 83 -> Test Accuracy: 42.06
[84, 60] loss: 1.424
[84, 120] loss: 1.393
[84, 180] loss: 1.414
[84, 240] loss: 1.426
[84, 300] loss: 1.430
[84, 360] loss: 1.414
Epoch: 84 -> Loss: 1.31609892845
Epoch: 84 -> Test Accuracy: 41.9
[85, 60] loss: 1.404
[85, 120] loss: 1.409
[85, 180] loss: 1.432
[85, 240] loss: 1.419
[85, 300] loss: 1.414
[85, 360] loss: 1.417
Epoch: 85 -> Loss: 1.54289126396
Epoch: 85 -> Test Accuracy: 42.53
[86, 60] loss: 1.405
[86, 120] loss: 1.404
[86, 180] loss: 1.381
[86, 240] loss: 1.367
[86, 300] loss: 1.392
[86, 360] loss: 1.386
Epoch: 86 -> Loss: 1.40317416191
Epoch: 86 -> Test Accuracy: 43.37
[87, 60] loss: 1.387
[87, 120] loss: 1.392
[87, 180] loss: 1.383
[87, 240] loss: 1.388
[87, 300] loss: 1.392
[87, 360] loss: 1.384
Epoch: 87 -> Loss: 1.14942085743
Epoch: 87 -> Test Accuracy: 43.66
[88, 60] loss: 1.375
[88, 120] loss: 1.392
[88, 180] loss: 1.362
[88, 240] loss: 1.390
[88, 300] loss: 1.385
[88, 360] loss: 1.393
Epoch: 88 -> Loss: 1.40160965919
Epoch: 88 -> Test Accuracy: 43.63
[89, 60] loss: 1.380
[89, 120] loss: 1.368
[89, 180] loss: 1.383
[89, 240] loss: 1.389
[89, 300] loss: 1.370
[89, 360] loss: 1.382
Epoch: 89 -> Loss: 1.53526997566
Epoch: 89 -> Test Accuracy: 43.54
[90, 60] loss: 1.373
[90, 120] loss: 1.366
[90, 180] loss: 1.388
[90, 240] loss: 1.379
[90, 300] loss: 1.400
[90, 360] loss: 1.375
Epoch: 90 -> Loss: 1.29325318336
Epoch: 90 -> Test Accuracy: 43.5
[91, 60] loss: 1.378
[91, 120] loss: 1.367
[91, 180] loss: 1.393
[91, 240] loss: 1.391
[91, 300] loss: 1.387
[91, 360] loss: 1.379
Epoch: 91 -> Loss: 1.52791833878
Epoch: 91 -> Test Accuracy: 43.51
[92, 60] loss: 1.384
[92, 120] loss: 1.378
[92, 180] loss: 1.388
[92, 240] loss: 1.381
[92, 300] loss: 1.371
[92, 360] loss: 1.373
Epoch: 92 -> Loss: 1.41006708145
Epoch: 92 -> Test Accuracy: 43.55
[93, 60] loss: 1.382
[93, 120] loss: 1.377
[93, 180] loss: 1.377
[93, 240] loss: 1.377
[93, 300] loss: 1.393
[93, 360] loss: 1.392
Epoch: 93 -> Loss: 1.23832499981
Epoch: 93 -> Test Accuracy: 43.65
[94, 60] loss: 1.377
[94, 120] loss: 1.370
[94, 180] loss: 1.390
[94, 240] loss: 1.392
[94, 300] loss: 1.394
[94, 360] loss: 1.381
Epoch: 94 -> Loss: 1.4056199789
Epoch: 94 -> Test Accuracy: 43.52
[95, 60] loss: 1.385
[95, 120] loss: 1.379
[95, 180] loss: 1.367
[95, 240] loss: 1.388
[95, 300] loss: 1.377
[95, 360] loss: 1.363
Epoch: 95 -> Loss: 1.36497354507
Epoch: 95 -> Test Accuracy: 43.56
[96, 60] loss: 1.369
[96, 120] loss: 1.377
[96, 180] loss: 1.368
[96, 240] loss: 1.380
[96, 300] loss: 1.367
[96, 360] loss: 1.367
Epoch: 96 -> Loss: 1.27985405922
Epoch: 96 -> Test Accuracy: 43.78
[97, 60] loss: 1.359
[97, 120] loss: 1.386
[97, 180] loss: 1.391
[97, 240] loss: 1.372
[97, 300] loss: 1.385
[97, 360] loss: 1.356
Epoch: 97 -> Loss: 1.41945564747
Epoch: 97 -> Test Accuracy: 43.38
[98, 60] loss: 1.380
[98, 120] loss: 1.386
[98, 180] loss: 1.381
[98, 240] loss: 1.387
[98, 300] loss: 1.365
[98, 360] loss: 1.384
Epoch: 98 -> Loss: 1.42576658726
Epoch: 98 -> Test Accuracy: 43.45
[99, 60] loss: 1.368
[99, 120] loss: 1.370
[99, 180] loss: 1.392
[99, 240] loss: 1.380
[99, 300] loss: 1.370
[99, 360] loss: 1.386
Epoch: 99 -> Loss: 1.31827950478
Epoch: 99 -> Test Accuracy: 43.78
[100, 60] loss: 1.374
[100, 120] loss: 1.376
[100, 180] loss: 1.360
[100, 240] loss: 1.366
[100, 300] loss: 1.375
[100, 360] loss: 1.381
Epoch: 100 -> Loss: 1.44635605812
Epoch: 100 -> Test Accuracy: 43.9
Finished Training
In [10]:
# save variables
fm.save_variable([rot_block5_loss_log, rot_block5_test_accuracy_log, 
                  block5_loss_log, block5_test_accuracy_log, 
                  conv_block5_loss_log, conv_block5_test_accuracy_log], "5_block_net")
In [11]:
# rename files
fm.add_block_to_name(5, [100, 200])

Supervised NIN

Note: In the code of the paper a 3 convolutional block RotNet was used for the classification task.

In [9]:
# initialize networks
net_class = RN.RotNet(num_classes=10, num_conv_block=3, add_avg_pool=False)
In [10]:
# train 3 block RotNet on classification task
class_NIN_loss_log, _, class_NIN_test_accuracy_log, _, _ = tr.adaptive_learning([0.1, 0.02, 0.004, 0.0008], 
    [60, 120, 160, 200], 0.9, 5e-4, net_class, criterion, trainloader, None, testloader)
[1, 60] loss: 1.751
[1, 120] loss: 1.480
[1, 180] loss: 1.339
[1, 240] loss: 1.254
[1, 300] loss: 1.175
[1, 360] loss: 1.112
Epoch: 1 -> Loss: 0.825036227703
Epoch: 1 -> Test Accuracy: 60.62
[2, 60] loss: 1.038
[2, 120] loss: 0.998
[2, 180] loss: 0.963
[2, 240] loss: 0.930
[2, 300] loss: 0.911
[2, 360] loss: 0.883
Epoch: 2 -> Loss: 0.775784909725
Epoch: 2 -> Test Accuracy: 69.0
[3, 60] loss: 0.800
[3, 120] loss: 0.807
[3, 180] loss: 0.783
[3, 240] loss: 0.803
[3, 300] loss: 0.791
[3, 360] loss: 0.775
Epoch: 3 -> Loss: 0.804202079773
Epoch: 3 -> Test Accuracy: 72.79
[4, 60] loss: 0.708
[4, 120] loss: 0.718
[4, 180] loss: 0.737
[4, 240] loss: 0.714
[4, 300] loss: 0.705
[4, 360] loss: 0.708
Epoch: 4 -> Loss: 0.555963754654
Epoch: 4 -> Test Accuracy: 75.21
[5, 60] loss: 0.648
[5, 120] loss: 0.670
[5, 180] loss: 0.670
[5, 240] loss: 0.658
[5, 300] loss: 0.669
[5, 360] loss: 0.655
Epoch: 5 -> Loss: 0.522774040699
Epoch: 5 -> Test Accuracy: 75.59
[6, 60] loss: 0.632
[6, 120] loss: 0.638
[6, 180] loss: 0.635
[6, 240] loss: 0.631
[6, 300] loss: 0.636
[6, 360] loss: 0.617
Epoch: 6 -> Loss: 0.645114660263
Epoch: 6 -> Test Accuracy: 77.1
[7, 60] loss: 0.598
[7, 120] loss: 0.603
[7, 180] loss: 0.604
[7, 240] loss: 0.593
[7, 300] loss: 0.605
[7, 360] loss: 0.594
Epoch: 7 -> Loss: 0.616147696972
Epoch: 7 -> Test Accuracy: 77.37
[8, 60] loss: 0.588
[8, 120] loss: 0.577
[8, 180] loss: 0.567
[8, 240] loss: 0.556
[8, 300] loss: 0.587
[8, 360] loss: 0.568
Epoch: 8 -> Loss: 0.573764920235
Epoch: 8 -> Test Accuracy: 78.65
[9, 60] loss: 0.524
[9, 120] loss: 0.565
[9, 180] loss: 0.547
[9, 240] loss: 0.570
[9, 300] loss: 0.547
[9, 360] loss: 0.549
Epoch: 9 -> Loss: 0.599540233612
Epoch: 9 -> Test Accuracy: 79.55
[10, 60] loss: 0.528
[10, 120] loss: 0.548
[10, 180] loss: 0.523
[10, 240] loss: 0.549
[10, 300] loss: 0.543
[10, 360] loss: 0.568
Epoch: 10 -> Loss: 0.459079831839
Epoch: 10 -> Test Accuracy: 79.53
[11, 60] loss: 0.512
[11, 120] loss: 0.509
[11, 180] loss: 0.520
[11, 240] loss: 0.528
[11, 300] loss: 0.552
[11, 360] loss: 0.524
Epoch: 11 -> Loss: 0.526621758938
Epoch: 11 -> Test Accuracy: 79.39
[12, 60] loss: 0.503
[12, 120] loss: 0.503
[12, 180] loss: 0.491
[12, 240] loss: 0.546
[12, 300] loss: 0.506
[12, 360] loss: 0.537
Epoch: 12 -> Loss: 0.492984056473
Epoch: 12 -> Test Accuracy: 80.46
[13, 60] loss: 0.488
[13, 120] loss: 0.493
[13, 180] loss: 0.512
[13, 240] loss: 0.502
[13, 300] loss: 0.521
[13, 360] loss: 0.511
Epoch: 13 -> Loss: 0.88372194767
Epoch: 13 -> Test Accuracy: 80.23
[14, 60] loss: 0.489
[14, 120] loss: 0.492
[14, 180] loss: 0.496
[14, 240] loss: 0.493
[14, 300] loss: 0.504
[14, 360] loss: 0.482
Epoch: 14 -> Loss: 0.431423246861
Epoch: 14 -> Test Accuracy: 80.05
[15, 60] loss: 0.496
[15, 120] loss: 0.476
[15, 180] loss: 0.500
[15, 240] loss: 0.499
[15, 300] loss: 0.473
[15, 360] loss: 0.502
Epoch: 15 -> Loss: 0.477042138577
Epoch: 15 -> Test Accuracy: 80.66
[16, 60] loss: 0.460
[16, 120] loss: 0.495
[16, 180] loss: 0.456
[16, 240] loss: 0.480
[16, 300] loss: 0.463
[16, 360] loss: 0.492
Epoch: 16 -> Loss: 0.489493042231
Epoch: 16 -> Test Accuracy: 81.42
[17, 60] loss: 0.481
[17, 120] loss: 0.447
[17, 180] loss: 0.472
[17, 240] loss: 0.449
[17, 300] loss: 0.476
[17, 360] loss: 0.476
Epoch: 17 -> Loss: 0.366201579571
Epoch: 17 -> Test Accuracy: 81.96
[18, 60] loss: 0.459
[18, 120] loss: 0.470
[18, 180] loss: 0.490
[18, 240] loss: 0.457
[18, 300] loss: 0.471
[18, 360] loss: 0.464
Epoch: 18 -> Loss: 0.637535214424
Epoch: 18 -> Test Accuracy: 82.02
[19, 60] loss: 0.436
[19, 120] loss: 0.467
[19, 180] loss: 0.462
[19, 240] loss: 0.464
[19, 300] loss: 0.457
[19, 360] loss: 0.468
Epoch: 19 -> Loss: 0.519069314003
Epoch: 19 -> Test Accuracy: 82.46
[20, 60] loss: 0.462
[20, 120] loss: 0.441
[20, 180] loss: 0.461
[20, 240] loss: 0.458
[20, 300] loss: 0.445
[20, 360] loss: 0.462
Epoch: 20 -> Loss: 0.585631966591
Epoch: 20 -> Test Accuracy: 82.5
[21, 60] loss: 0.439
[21, 120] loss: 0.439
[21, 180] loss: 0.454
[21, 240] loss: 0.454
[21, 300] loss: 0.471
[21, 360] loss: 0.434
Epoch: 21 -> Loss: 0.443163454533
Epoch: 21 -> Test Accuracy: 81.27
[22, 60] loss: 0.407
[22, 120] loss: 0.436
[22, 180] loss: 0.473
[22, 240] loss: 0.449
[22, 300] loss: 0.457
[22, 360] loss: 0.440
Epoch: 22 -> Loss: 0.397458344698
Epoch: 22 -> Test Accuracy: 82.7
[23, 60] loss: 0.421
[23, 120] loss: 0.423
[23, 180] loss: 0.455
[23, 240] loss: 0.463
[23, 300] loss: 0.432
[23, 360] loss: 0.454
Epoch: 23 -> Loss: 0.573688149452
Epoch: 23 -> Test Accuracy: 81.06
[24, 60] loss: 0.425
[24, 120] loss: 0.408
[24, 180] loss: 0.464
[24, 240] loss: 0.438
[24, 300] loss: 0.448
[24, 360] loss: 0.435
Epoch: 24 -> Loss: 0.485114812851
Epoch: 24 -> Test Accuracy: 82.19
[25, 60] loss: 0.425
[25, 120] loss: 0.403
[25, 180] loss: 0.429
[25, 240] loss: 0.438
[25, 300] loss: 0.437
[25, 360] loss: 0.459
Epoch: 25 -> Loss: 0.451935589314
Epoch: 25 -> Test Accuracy: 82.87
[26, 60] loss: 0.432
[26, 120] loss: 0.423
[26, 180] loss: 0.420
[26, 240] loss: 0.435
[26, 300] loss: 0.446
[26, 360] loss: 0.417
Epoch: 26 -> Loss: 0.521824896336
Epoch: 26 -> Test Accuracy: 83.0
[27, 60] loss: 0.420
[27, 120] loss: 0.419
[27, 180] loss: 0.432
[27, 240] loss: 0.444
[27, 300] loss: 0.419
[27, 360] loss: 0.461
Epoch: 27 -> Loss: 0.448279857635
Epoch: 27 -> Test Accuracy: 82.64
[28, 60] loss: 0.402
[28, 120] loss: 0.403
[28, 180] loss: 0.417
[28, 240] loss: 0.444
[28, 300] loss: 0.426
[28, 360] loss: 0.435
Epoch: 28 -> Loss: 0.296328753233
Epoch: 28 -> Test Accuracy: 83.38
[29, 60] loss: 0.413
[29, 120] loss: 0.423
[29, 180] loss: 0.419
[29, 240] loss: 0.417
[29, 300] loss: 0.442
[29, 360] loss: 0.436
Epoch: 29 -> Loss: 0.414182603359
Epoch: 29 -> Test Accuracy: 82.96
[30, 60] loss: 0.396
[30, 120] loss: 0.418
[30, 180] loss: 0.409
[30, 240] loss: 0.423
[30, 300] loss: 0.435
[30, 360] loss: 0.423
Epoch: 30 -> Loss: 0.492857694626
Epoch: 30 -> Test Accuracy: 82.43
[31, 60] loss: 0.415
[31, 120] loss: 0.407
[31, 180] loss: 0.405
[31, 240] loss: 0.409
[31, 300] loss: 0.420
[31, 360] loss: 0.429
Epoch: 31 -> Loss: 0.408995449543
Epoch: 31 -> Test Accuracy: 82.86
[32, 60] loss: 0.411
[32, 120] loss: 0.404
[32, 180] loss: 0.407
[32, 240] loss: 0.423
[32, 300] loss: 0.411
[32, 360] loss: 0.425
Epoch: 32 -> Loss: 0.489699691534
Epoch: 32 -> Test Accuracy: 84.13
[33, 60] loss: 0.380
[33, 120] loss: 0.398
[33, 180] loss: 0.428
[33, 240] loss: 0.424
[33, 300] loss: 0.423
[33, 360] loss: 0.424
Epoch: 33 -> Loss: 0.340313047171
Epoch: 33 -> Test Accuracy: 82.46
[34, 60] loss: 0.399
[34, 120] loss: 0.399
[34, 180] loss: 0.419
[34, 240] loss: 0.405
[34, 300] loss: 0.422
[34, 360] loss: 0.435
Epoch: 34 -> Loss: 0.443888813257
Epoch: 34 -> Test Accuracy: 84.08
[35, 60] loss: 0.397
[35, 120] loss: 0.399
[35, 180] loss: 0.400
[35, 240] loss: 0.437
[35, 300] loss: 0.420
[35, 360] loss: 0.411
Epoch: 35 -> Loss: 0.550703644753
Epoch: 35 -> Test Accuracy: 84.29
[36, 60] loss: 0.378
[36, 120] loss: 0.415
[36, 180] loss: 0.401
[36, 240] loss: 0.413
[36, 300] loss: 0.418
[36, 360] loss: 0.405
Epoch: 36 -> Loss: 0.388389289379
Epoch: 36 -> Test Accuracy: 82.57
[37, 60] loss: 0.395
[37, 120] loss: 0.403
[37, 180] loss: 0.417
[37, 240] loss: 0.433
[37, 300] loss: 0.428
[37, 360] loss: 0.420
Epoch: 37 -> Loss: 0.444215625525
Epoch: 37 -> Test Accuracy: 83.28
[38, 60] loss: 0.387
[38, 120] loss: 0.392
[38, 180] loss: 0.388
[38, 240] loss: 0.398
[38, 300] loss: 0.419
[38, 360] loss: 0.420
Epoch: 38 -> Loss: 0.378732860088
Epoch: 38 -> Test Accuracy: 82.44
[39, 60] loss: 0.387
[39, 120] loss: 0.380
[39, 180] loss: 0.399
[39, 240] loss: 0.414
[39, 300] loss: 0.413
[39, 360] loss: 0.404
Epoch: 39 -> Loss: 0.441468238831
Epoch: 39 -> Test Accuracy: 83.92
[40, 60] loss: 0.391
[40, 120] loss: 0.384
[40, 180] loss: 0.414
[40, 240] loss: 0.404
[40, 300] loss: 0.423
[40, 360] loss: 0.417
Epoch: 40 -> Loss: 0.44526296854
Epoch: 40 -> Test Accuracy: 83.79
[41, 60] loss: 0.391
[41, 120] loss: 0.384
[41, 180] loss: 0.414
[41, 240] loss: 0.414
[41, 300] loss: 0.413
[41, 360] loss: 0.408
Epoch: 41 -> Loss: 0.379072278738
Epoch: 41 -> Test Accuracy: 83.75
[42, 60] loss: 0.379
[42, 120] loss: 0.404
[42, 180] loss: 0.400
[42, 240] loss: 0.424
[42, 300] loss: 0.414
[42, 360] loss: 0.398
Epoch: 42 -> Loss: 0.593099057674
Epoch: 42 -> Test Accuracy: 83.77
[43, 60] loss: 0.380
[43, 120] loss: 0.389
[43, 180] loss: 0.401
[43, 240] loss: 0.431
[43, 300] loss: 0.406
[43, 360] loss: 0.399
Epoch: 43 -> Loss: 0.20330825448
Epoch: 43 -> Test Accuracy: 83.59
[44, 60] loss: 0.359
[44, 120] loss: 0.397
[44, 180] loss: 0.400
[44, 240] loss: 0.403
[44, 300] loss: 0.405
[44, 360] loss: 0.407
Epoch: 44 -> Loss: 0.428490787745
Epoch: 44 -> Test Accuracy: 84.12
[45, 60] loss: 0.389
[45, 120] loss: 0.412
[45, 180] loss: 0.382
[45, 240] loss: 0.397
[45, 300] loss: 0.401
[45, 360] loss: 0.413
Epoch: 45 -> Loss: 0.486002355814
Epoch: 45 -> Test Accuracy: 83.85
[46, 60] loss: 0.398
[46, 120] loss: 0.382
[46, 180] loss: 0.416
[46, 240] loss: 0.384
[46, 300] loss: 0.406
[46, 360] loss: 0.422
Epoch: 46 -> Loss: 0.473516404629
Epoch: 46 -> Test Accuracy: 82.9
[47, 60] loss: 0.363
[47, 120] loss: 0.402
[47, 180] loss: 0.396
[47, 240] loss: 0.389
[47, 300] loss: 0.410
[47, 360] loss: 0.407
Epoch: 47 -> Loss: 0.30972841382
Epoch: 47 -> Test Accuracy: 83.93
[48, 60] loss: 0.361
[48, 120] loss: 0.376
[48, 180] loss: 0.410
[48, 240] loss: 0.422
[48, 300] loss: 0.400
[48, 360] loss: 0.403
Epoch: 48 -> Loss: 0.2999766469
Epoch: 48 -> Test Accuracy: 84.31
[49, 60] loss: 0.394
[49, 120] loss: 0.380
[49, 180] loss: 0.405
[49, 240] loss: 0.398
[49, 300] loss: 0.404
[49, 360] loss: 0.381
Epoch: 49 -> Loss: 0.502337992191
Epoch: 49 -> Test Accuracy: 82.65
[50, 60] loss: 0.372
[50, 120] loss: 0.378
[50, 180] loss: 0.397
[50, 240] loss: 0.400
[50, 300] loss: 0.384
[50, 360] loss: 0.408
Epoch: 50 -> Loss: 0.469843149185
Epoch: 50 -> Test Accuracy: 85.27
[51, 60] loss: 0.368
[51, 120] loss: 0.398
[51, 180] loss: 0.380
[51, 240] loss: 0.404
[51, 300] loss: 0.403
[51, 360] loss: 0.399
Epoch: 51 -> Loss: 0.491511195898
Epoch: 51 -> Test Accuracy: 84.14
[52, 60] loss: 0.367
[52, 120] loss: 0.372
[52, 180] loss: 0.385
[52, 240] loss: 0.417
[52, 300] loss: 0.391
[52, 360] loss: 0.391
Epoch: 52 -> Loss: 0.439156144857
Epoch: 52 -> Test Accuracy: 84.07
[53, 60] loss: 0.371
[53, 120] loss: 0.373
[53, 180] loss: 0.390
[53, 240] loss: 0.409
[53, 300] loss: 0.411
[53, 360] loss: 0.411
Epoch: 53 -> Loss: 0.396387606859
Epoch: 53 -> Test Accuracy: 83.2
[54, 60] loss: 0.372
[54, 120] loss: 0.362
[54, 180] loss: 0.393
[54, 240] loss: 0.407
[54, 300] loss: 0.382
[54, 360] loss: 0.420
Epoch: 54 -> Loss: 0.492816776037
Epoch: 54 -> Test Accuracy: 83.64
[55, 60] loss: 0.379
[55, 120] loss: 0.402
[55, 180] loss: 0.403
[55, 240] loss: 0.368
[55, 300] loss: 0.382
[55, 360] loss: 0.406
Epoch: 55 -> Loss: 0.495667219162
Epoch: 55 -> Test Accuracy: 84.79
[56, 60] loss: 0.371
[56, 120] loss: 0.376
[56, 180] loss: 0.375
[56, 240] loss: 0.386
[56, 300] loss: 0.389
[56, 360] loss: 0.409
Epoch: 56 -> Loss: 0.326176345348
Epoch: 56 -> Test Accuracy: 84.16
[57, 60] loss: 0.376
[57, 120] loss: 0.373
[57, 180] loss: 0.389
[57, 240] loss: 0.400
[57, 300] loss: 0.388
[57, 360] loss: 0.378
Epoch: 57 -> Loss: 0.519686937332
Epoch: 57 -> Test Accuracy: 83.23
[58, 60] loss: 0.384
[58, 120] loss: 0.358
[58, 180] loss: 0.385
[58, 240] loss: 0.382
[58, 300] loss: 0.399
[58, 360] loss: 0.410
Epoch: 58 -> Loss: 0.343048483133
Epoch: 58 -> Test Accuracy: 84.65
[59, 60] loss: 0.359
[59, 120] loss: 0.384
[59, 180] loss: 0.395
[59, 240] loss: 0.391
[59, 300] loss: 0.385
[59, 360] loss: 0.396
Epoch: 59 -> Loss: 0.461993128061
Epoch: 59 -> Test Accuracy: 84.73
[60, 60] loss: 0.362
[60, 120] loss: 0.391
[60, 180] loss: 0.386
[60, 240] loss: 0.398
[60, 300] loss: 0.393
[60, 360] loss: 0.380
Epoch: 60 -> Loss: 0.526801228523
Epoch: 60 -> Test Accuracy: 81.88
[61, 60] loss: 0.280
[61, 120] loss: 0.221
[61, 180] loss: 0.220
[61, 240] loss: 0.225
[61, 300] loss: 0.212
[61, 360] loss: 0.205
Epoch: 61 -> Loss: 0.132441371679
Epoch: 61 -> Test Accuracy: 89.27
[62, 60] loss: 0.168
[62, 120] loss: 0.172
[62, 180] loss: 0.173
[62, 240] loss: 0.174
[62, 300] loss: 0.188
[62, 360] loss: 0.181
Epoch: 62 -> Loss: 0.262564599514
Epoch: 62 -> Test Accuracy: 89.49
[63, 60] loss: 0.152
[63, 120] loss: 0.166
[63, 180] loss: 0.161
[63, 240] loss: 0.155
[63, 300] loss: 0.162
[63, 360] loss: 0.162
Epoch: 63 -> Loss: 0.164224550128
Epoch: 63 -> Test Accuracy: 89.29
[64, 60] loss: 0.137
[64, 120] loss: 0.147
[64, 180] loss: 0.144
[64, 240] loss: 0.157
[64, 300] loss: 0.156
[64, 360] loss: 0.149
Epoch: 64 -> Loss: 0.199184060097
Epoch: 64 -> Test Accuracy: 89.32
[65, 60] loss: 0.124
[65, 120] loss: 0.140
[65, 180] loss: 0.148
[65, 240] loss: 0.131
[65, 300] loss: 0.153
[65, 360] loss: 0.149
Epoch: 65 -> Loss: 0.127772569656
Epoch: 65 -> Test Accuracy: 89.05
[66, 60] loss: 0.118
[66, 120] loss: 0.131
[66, 180] loss: 0.138
[66, 240] loss: 0.133
[66, 300] loss: 0.139
[66, 360] loss: 0.148
Epoch: 66 -> Loss: 0.161133691669
Epoch: 66 -> Test Accuracy: 89.35
[67, 60] loss: 0.134
[67, 120] loss: 0.125
[67, 180] loss: 0.116
[67, 240] loss: 0.138
[67, 300] loss: 0.141
[67, 360] loss: 0.137
Epoch: 67 -> Loss: 0.153490871191
Epoch: 67 -> Test Accuracy: 89.13
[68, 60] loss: 0.126
[68, 120] loss: 0.121
[68, 180] loss: 0.128
[68, 240] loss: 0.133
[68, 300] loss: 0.129
[68, 360] loss: 0.137
Epoch: 68 -> Loss: 0.109140112996
Epoch: 68 -> Test Accuracy: 88.57
[69, 60] loss: 0.122
[69, 120] loss: 0.120
[69, 180] loss: 0.123
[69, 240] loss: 0.130
[69, 300] loss: 0.146
[69, 360] loss: 0.140
Epoch: 69 -> Loss: 0.161554858088
Epoch: 69 -> Test Accuracy: 89.36
[70, 60] loss: 0.120
[70, 120] loss: 0.112
[70, 180] loss: 0.129
[70, 240] loss: 0.130
[70, 300] loss: 0.132
[70, 360] loss: 0.138
Epoch: 70 -> Loss: 0.102998875082
Epoch: 70 -> Test Accuracy: 88.62
[71, 60] loss: 0.130
[71, 120] loss: 0.116
[71, 180] loss: 0.129
[71, 240] loss: 0.134
[71, 300] loss: 0.154
[71, 360] loss: 0.139
Epoch: 71 -> Loss: 0.152618929744
Epoch: 71 -> Test Accuracy: 88.61
[72, 60] loss: 0.118
[72, 120] loss: 0.119
[72, 180] loss: 0.134
[72, 240] loss: 0.139
[72, 300] loss: 0.149
[72, 360] loss: 0.160
Epoch: 72 -> Loss: 0.129081323743
Epoch: 72 -> Test Accuracy: 88.7
[73, 60] loss: 0.121
[73, 120] loss: 0.134
[73, 180] loss: 0.135
[73, 240] loss: 0.123
[73, 300] loss: 0.140
[73, 360] loss: 0.133
Epoch: 73 -> Loss: 0.137998253107
Epoch: 73 -> Test Accuracy: 88.36
[74, 60] loss: 0.118
[74, 120] loss: 0.131
[74, 180] loss: 0.118
[74, 240] loss: 0.142
[74, 300] loss: 0.147
[74, 360] loss: 0.139
Epoch: 74 -> Loss: 0.0809362605214
Epoch: 74 -> Test Accuracy: 88.13
[75, 60] loss: 0.124
[75, 120] loss: 0.141
[75, 180] loss: 0.150
[75, 240] loss: 0.143
[75, 300] loss: 0.158
[75, 360] loss: 0.156
Epoch: 75 -> Loss: 0.30938565731
Epoch: 75 -> Test Accuracy: 87.59
[76, 60] loss: 0.123
[76, 120] loss: 0.126
[76, 180] loss: 0.134
[76, 240] loss: 0.132
[76, 300] loss: 0.144
[76, 360] loss: 0.143
Epoch: 76 -> Loss: 0.158531919122
Epoch: 76 -> Test Accuracy: 88.79
[77, 60] loss: 0.121
[77, 120] loss: 0.130
[77, 180] loss: 0.139
[77, 240] loss: 0.139
[77, 300] loss: 0.147
[77, 360] loss: 0.150
Epoch: 77 -> Loss: 0.077156893909
Epoch: 77 -> Test Accuracy: 88.3
[78, 60] loss: 0.114
[78, 120] loss: 0.127
[78, 180] loss: 0.122
[78, 240] loss: 0.136
[78, 300] loss: 0.141
[78, 360] loss: 0.156
Epoch: 78 -> Loss: 0.336257785559
Epoch: 78 -> Test Accuracy: 87.83
[79, 60] loss: 0.147
[79, 120] loss: 0.124
[79, 180] loss: 0.135
[79, 240] loss: 0.141
[79, 300] loss: 0.143
[79, 360] loss: 0.153
Epoch: 79 -> Loss: 0.121548376977
Epoch: 79 -> Test Accuracy: 87.59
[80, 60] loss: 0.131
[80, 120] loss: 0.127
[80, 180] loss: 0.125
[80, 240] loss: 0.139
[80, 300] loss: 0.152
[80, 360] loss: 0.163
Epoch: 80 -> Loss: 0.0924355834723
Epoch: 80 -> Test Accuracy: 88.42
[81, 60] loss: 0.125
[81, 120] loss: 0.123
[81, 180] loss: 0.125
[81, 240] loss: 0.140
[81, 300] loss: 0.143
[81, 360] loss: 0.139
Epoch: 81 -> Loss: 0.144900158048
Epoch: 81 -> Test Accuracy: 89.16
[82, 60] loss: 0.118
[82, 120] loss: 0.128
[82, 180] loss: 0.138
[82, 240] loss: 0.148
[82, 300] loss: 0.148
[82, 360] loss: 0.133
Epoch: 82 -> Loss: 0.26424741745
Epoch: 82 -> Test Accuracy: 87.97
[83, 60] loss: 0.114
[83, 120] loss: 0.126
[83, 180] loss: 0.129
[83, 240] loss: 0.136
[83, 300] loss: 0.133
[83, 360] loss: 0.149
Epoch: 83 -> Loss: 0.19867387414
Epoch: 83 -> Test Accuracy: 88.52
[84, 60] loss: 0.114
[84, 120] loss: 0.121
[84, 180] loss: 0.132
[84, 240] loss: 0.132
[84, 300] loss: 0.142
[84, 360] loss: 0.158
Epoch: 84 -> Loss: 0.0979826822877
Epoch: 84 -> Test Accuracy: 88.32
[85, 60] loss: 0.120
[85, 120] loss: 0.130
[85, 180] loss: 0.140
[85, 240] loss: 0.146
[85, 300] loss: 0.142
[85, 360] loss: 0.143
Epoch: 85 -> Loss: 0.166171133518
Epoch: 85 -> Test Accuracy: 88.5
[86, 60] loss: 0.119
[86, 120] loss: 0.122
[86, 180] loss: 0.136
[86, 240] loss: 0.129
[86, 300] loss: 0.135
[86, 360] loss: 0.156
Epoch: 86 -> Loss: 0.151281192899
Epoch: 86 -> Test Accuracy: 88.61
[87, 60] loss: 0.120
[87, 120] loss: 0.109
[87, 180] loss: 0.132
[87, 240] loss: 0.128
[87, 300] loss: 0.143
[87, 360] loss: 0.142
Epoch: 87 -> Loss: 0.173461005092
Epoch: 87 -> Test Accuracy: 88.67
[88, 60] loss: 0.116
[88, 120] loss: 0.128
[88, 180] loss: 0.130
[88, 240] loss: 0.139
[88, 300] loss: 0.140
[88, 360] loss: 0.140
Epoch: 88 -> Loss: 0.225553706288
Epoch: 88 -> Test Accuracy: 88.07
[89, 60] loss: 0.113
[89, 120] loss: 0.125
[89, 180] loss: 0.127
[89, 240] loss: 0.136
[89, 300] loss: 0.139
[89, 360] loss: 0.137
Epoch: 89 -> Loss: 0.165683954954
Epoch: 89 -> Test Accuracy: 87.6
[90, 60] loss: 0.106
[90, 120] loss: 0.113
[90, 180] loss: 0.122
[90, 240] loss: 0.137
[90, 300] loss: 0.142
[90, 360] loss: 0.143
Epoch: 90 -> Loss: 0.0895455107093
Epoch: 90 -> Test Accuracy: 88.4
[91, 60] loss: 0.130
[91, 120] loss: 0.119
[91, 180] loss: 0.124
[91, 240] loss: 0.137
[91, 300] loss: 0.147
[91, 360] loss: 0.138
Epoch: 91 -> Loss: 0.221307352185
Epoch: 91 -> Test Accuracy: 87.88
[92, 60] loss: 0.111
[92, 120] loss: 0.126
[92, 180] loss: 0.133
[92, 240] loss: 0.129
[92, 300] loss: 0.136
[92, 360] loss: 0.138
Epoch: 92 -> Loss: 0.268699020147
Epoch: 92 -> Test Accuracy: 88.3
[93, 60] loss: 0.110
[93, 120] loss: 0.115
[93, 180] loss: 0.137
[93, 240] loss: 0.123
[93, 300] loss: 0.136
[93, 360] loss: 0.133
Epoch: 93 -> Loss: 0.0883329063654
Epoch: 93 -> Test Accuracy: 87.29
[94, 60] loss: 0.134
[94, 120] loss: 0.125
[94, 180] loss: 0.128
[94, 240] loss: 0.124
[94, 300] loss: 0.128
[94, 360] loss: 0.137
Epoch: 94 -> Loss: 0.0805646926165
Epoch: 94 -> Test Accuracy: 88.1
[95, 60] loss: 0.104
[95, 120] loss: 0.120
[95, 180] loss: 0.114
[95, 240] loss: 0.135
[95, 300] loss: 0.135
[95, 360] loss: 0.139
Epoch: 95 -> Loss: 0.18209400773
Epoch: 95 -> Test Accuracy: 88.31
[96, 60] loss: 0.116
[96, 120] loss: 0.110
[96, 180] loss: 0.122
[96, 240] loss: 0.127
[96, 300] loss: 0.136
[96, 360] loss: 0.130
Epoch: 96 -> Loss: 0.0532575622201
Epoch: 96 -> Test Accuracy: 88.14
[97, 60] loss: 0.124
[97, 120] loss: 0.130
[97, 180] loss: 0.111
[97, 240] loss: 0.112
[97, 300] loss: 0.125
[97, 360] loss: 0.142
Epoch: 97 -> Loss: 0.068568430841
Epoch: 97 -> Test Accuracy: 88.73
[98, 60] loss: 0.114
[98, 120] loss: 0.112
[98, 180] loss: 0.123
[98, 240] loss: 0.115
[98, 300] loss: 0.132
[98, 360] loss: 0.140
Epoch: 98 -> Loss: 0.0740065425634
Epoch: 98 -> Test Accuracy: 88.96
[99, 60] loss: 0.116
[99, 120] loss: 0.118
[99, 180] loss: 0.119
[99, 240] loss: 0.122
[99, 300] loss: 0.134
[99, 360] loss: 0.150
Epoch: 99 -> Loss: 0.247417330742
Epoch: 99 -> Test Accuracy: 87.67
[100, 60] loss: 0.129
[100, 120] loss: 0.113
[100, 180] loss: 0.112
[100, 240] loss: 0.136
[100, 300] loss: 0.129
[100, 360] loss: 0.130
Epoch: 100 -> Loss: 0.089723482728
Epoch: 100 -> Test Accuracy: 88.47
[101, 60] loss: 0.115
[101, 120] loss: 0.111
[101, 180] loss: 0.128
[101, 240] loss: 0.127
[101, 300] loss: 0.132
[101, 360] loss: 0.138
Epoch: 101 -> Loss: 0.0839704573154
Epoch: 101 -> Test Accuracy: 87.68
[102, 60] loss: 0.102
[102, 120] loss: 0.111
[102, 180] loss: 0.131
[102, 240] loss: 0.129
[102, 300] loss: 0.149
[102, 360] loss: 0.136
Epoch: 102 -> Loss: 0.243998855352
Epoch: 102 -> Test Accuracy: 88.61
[103, 60] loss: 0.108
[103, 120] loss: 0.103
[103, 180] loss: 0.110
[103, 240] loss: 0.125
[103, 300] loss: 0.127
[103, 360] loss: 0.141
Epoch: 103 -> Loss: 0.0751181691885
Epoch: 103 -> Test Accuracy: 88.23
[104, 60] loss: 0.104
[104, 120] loss: 0.108
[104, 180] loss: 0.118
[104, 240] loss: 0.121
[104, 300] loss: 0.128
[104, 360] loss: 0.125
Epoch: 104 -> Loss: 0.214813321829
Epoch: 104 -> Test Accuracy: 88.3
[105, 60] loss: 0.101
[105, 120] loss: 0.103
[105, 180] loss: 0.115
[105, 240] loss: 0.115
[105, 300] loss: 0.131
[105, 360] loss: 0.143
Epoch: 105 -> Loss: 0.0993434637785
Epoch: 105 -> Test Accuracy: 88.33
[106, 60] loss: 0.107
[106, 120] loss: 0.106
[106, 180] loss: 0.107
[106, 240] loss: 0.113
[106, 300] loss: 0.138
[106, 360] loss: 0.127
Epoch: 106 -> Loss: 0.0725574865937
Epoch: 106 -> Test Accuracy: 88.67
[107, 60] loss: 0.105
[107, 120] loss: 0.109
[107, 180] loss: 0.116
[107, 240] loss: 0.123
[107, 300] loss: 0.137
[107, 360] loss: 0.144
Epoch: 107 -> Loss: 0.144227355719
Epoch: 107 -> Test Accuracy: 88.15
[108, 60] loss: 0.112
[108, 120] loss: 0.117
[108, 180] loss: 0.124
[108, 240] loss: 0.120
[108, 300] loss: 0.118
[108, 360] loss: 0.136
Epoch: 108 -> Loss: 0.162149980664
Epoch: 108 -> Test Accuracy: 88.18
[109, 60] loss: 0.099
[109, 120] loss: 0.110
[109, 180] loss: 0.120
[109, 240] loss: 0.126
[109, 300] loss: 0.121
[109, 360] loss: 0.131
Epoch: 109 -> Loss: 0.138436213136
Epoch: 109 -> Test Accuracy: 88.29
[110, 60] loss: 0.106
[110, 120] loss: 0.102
[110, 180] loss: 0.112
[110, 240] loss: 0.108
[110, 300] loss: 0.118
[110, 360] loss: 0.121
Epoch: 110 -> Loss: 0.143770366907
Epoch: 110 -> Test Accuracy: 88.23
[111, 60] loss: 0.110
[111, 120] loss: 0.107
[111, 180] loss: 0.112
[111, 240] loss: 0.110
[111, 300] loss: 0.114
[111, 360] loss: 0.129
Epoch: 111 -> Loss: 0.0900486707687
Epoch: 111 -> Test Accuracy: 88.19
[112, 60] loss: 0.109
[112, 120] loss: 0.110
[112, 180] loss: 0.114
[112, 240] loss: 0.139
[112, 300] loss: 0.118
[112, 360] loss: 0.144
Epoch: 112 -> Loss: 0.154474571347
Epoch: 112 -> Test Accuracy: 88.19
[113, 60] loss: 0.117
[113, 120] loss: 0.109
[113, 180] loss: 0.108
[113, 240] loss: 0.112
[113, 300] loss: 0.122
[113, 360] loss: 0.144
Epoch: 113 -> Loss: 0.0912730693817
Epoch: 113 -> Test Accuracy: 88.45
[114, 60] loss: 0.110
[114, 120] loss: 0.099
[114, 180] loss: 0.115
[114, 240] loss: 0.118
[114, 300] loss: 0.119
[114, 360] loss: 0.124
Epoch: 114 -> Loss: 0.0848844274879
Epoch: 114 -> Test Accuracy: 88.53
[115, 60] loss: 0.117
[115, 120] loss: 0.120
[115, 180] loss: 0.108
[115, 240] loss: 0.121
[115, 300] loss: 0.129
[115, 360] loss: 0.141
Epoch: 115 -> Loss: 0.103536307812
Epoch: 115 -> Test Accuracy: 88.43
[116, 60] loss: 0.105
[116, 120] loss: 0.105
[116, 180] loss: 0.112
[116, 240] loss: 0.122
[116, 300] loss: 0.122
[116, 360] loss: 0.126
Epoch: 116 -> Loss: 0.16309531033
Epoch: 116 -> Test Accuracy: 87.61
[117, 60] loss: 0.103
[117, 120] loss: 0.114
[117, 180] loss: 0.112
[117, 240] loss: 0.111
[117, 300] loss: 0.123
[117, 360] loss: 0.138
Epoch: 117 -> Loss: 0.0620582923293
Epoch: 117 -> Test Accuracy: 88.72
[118, 60] loss: 0.103
[118, 120] loss: 0.113
[118, 180] loss: 0.116
[118, 240] loss: 0.108
[118, 300] loss: 0.116
[118, 360] loss: 0.115
Epoch: 118 -> Loss: 0.087500795722
Epoch: 118 -> Test Accuracy: 87.27
[119, 60] loss: 0.106
[119, 120] loss: 0.114
[119, 180] loss: 0.126
[119, 240] loss: 0.104
[119, 300] loss: 0.103
[119, 360] loss: 0.122
Epoch: 119 -> Loss: 0.0654944702983
Epoch: 119 -> Test Accuracy: 88.68
[120, 60] loss: 0.111
[120, 120] loss: 0.101
[120, 180] loss: 0.114
[120, 240] loss: 0.118
[120, 300] loss: 0.132
[120, 360] loss: 0.132
Epoch: 120 -> Loss: 0.14968085289
Epoch: 120 -> Test Accuracy: 88.67
[121, 60] loss: 0.075
[121, 120] loss: 0.054
[121, 180] loss: 0.051
[121, 240] loss: 0.042
[121, 300] loss: 0.044
[121, 360] loss: 0.042
Epoch: 121 -> Loss: 0.0615567862988
Epoch: 121 -> Test Accuracy: 91.2
[122, 60] loss: 0.031
[122, 120] loss: 0.034
[122, 180] loss: 0.030
[122, 240] loss: 0.029
[122, 300] loss: 0.031
[122, 360] loss: 0.032
Epoch: 122 -> Loss: 0.0204253550619
Epoch: 122 -> Test Accuracy: 91.56
[123, 60] loss: 0.025
[123, 120] loss: 0.026
[123, 180] loss: 0.026
[123, 240] loss: 0.026
[123, 300] loss: 0.025
[123, 360] loss: 0.026
Epoch: 123 -> Loss: 0.0279893390834
Epoch: 123 -> Test Accuracy: 91.57
[124, 60] loss: 0.022
[124, 120] loss: 0.022
[124, 180] loss: 0.021
[124, 240] loss: 0.020
[124, 300] loss: 0.020
[124, 360] loss: 0.022
Epoch: 124 -> Loss: 0.00913160480559
Epoch: 124 -> Test Accuracy: 91.3
[125, 60] loss: 0.019
[125, 120] loss: 0.019
[125, 180] loss: 0.019
[125, 240] loss: 0.020
[125, 300] loss: 0.020
[125, 360] loss: 0.021
Epoch: 125 -> Loss: 0.0156837590039
Epoch: 125 -> Test Accuracy: 91.25
[126, 60] loss: 0.020
[126, 120] loss: 0.019
[126, 180] loss: 0.019
[126, 240] loss: 0.018
[126, 300] loss: 0.015
[126, 360] loss: 0.020
Epoch: 126 -> Loss: 0.00777263054624
Epoch: 126 -> Test Accuracy: 91.32
[127, 60] loss: 0.016
[127, 120] loss: 0.017
[127, 180] loss: 0.016
[127, 240] loss: 0.016
[127, 300] loss: 0.018
[127, 360] loss: 0.017
Epoch: 127 -> Loss: 0.0105040315539
Epoch: 127 -> Test Accuracy: 91.21
[128, 60] loss: 0.016
[128, 120] loss: 0.016
[128, 180] loss: 0.016
[128, 240] loss: 0.015
[128, 300] loss: 0.015
[128, 360] loss: 0.015
Epoch: 128 -> Loss: 0.0326036699116
Epoch: 128 -> Test Accuracy: 91.28
[129, 60] loss: 0.016
[129, 120] loss: 0.015
[129, 180] loss: 0.015
[129, 240] loss: 0.015
[129, 300] loss: 0.016
[129, 360] loss: 0.016
Epoch: 129 -> Loss: 0.0272331349552
Epoch: 129 -> Test Accuracy: 91.44
[130, 60] loss: 0.014
[130, 120] loss: 0.014
[130, 180] loss: 0.014
[130, 240] loss: 0.016
[130, 300] loss: 0.015
[130, 360] loss: 0.015
Epoch: 130 -> Loss: 0.0103903533891
Epoch: 130 -> Test Accuracy: 91.29
[131, 60] loss: 0.013
[131, 120] loss: 0.014
[131, 180] loss: 0.014
[131, 240] loss: 0.014
[131, 300] loss: 0.013
[131, 360] loss: 0.015
Epoch: 131 -> Loss: 0.0119562689215
Epoch: 131 -> Test Accuracy: 91.25
[132, 60] loss: 0.013
[132, 120] loss: 0.015
[132, 180] loss: 0.013
[132, 240] loss: 0.014
[132, 300] loss: 0.013
[132, 360] loss: 0.014
Epoch: 132 -> Loss: 0.0209854543209
Epoch: 132 -> Test Accuracy: 91.25
[133, 60] loss: 0.014
[133, 120] loss: 0.013
[133, 180] loss: 0.012
[133, 240] loss: 0.013
[133, 300] loss: 0.012
[133, 360] loss: 0.012
Epoch: 133 -> Loss: 0.0246900375932
Epoch: 133 -> Test Accuracy: 91.32
[134, 60] loss: 0.012
[134, 120] loss: 0.011
[134, 180] loss: 0.013
[134, 240] loss: 0.012
[134, 300] loss: 0.012
[134, 360] loss: 0.014
Epoch: 134 -> Loss: 0.014113759622
Epoch: 134 -> Test Accuracy: 91.12
[135, 60] loss: 0.011
[135, 120] loss: 0.010
[135, 180] loss: 0.012
[135, 240] loss: 0.012
[135, 300] loss: 0.011
[135, 360] loss: 0.011
Epoch: 135 -> Loss: 0.0275889132172
Epoch: 135 -> Test Accuracy: 91.17
[136, 60] loss: 0.012
[136, 120] loss: 0.012
[136, 180] loss: 0.010
[136, 240] loss: 0.011
[136, 300] loss: 0.011
[136, 360] loss: 0.011
Epoch: 136 -> Loss: 0.0197756737471
Epoch: 136 -> Test Accuracy: 91.07
[137, 60] loss: 0.011
[137, 120] loss: 0.011
[137, 180] loss: 0.011
[137, 240] loss: 0.011
[137, 300] loss: 0.010
[137, 360] loss: 0.011
Epoch: 137 -> Loss: 0.0155540648848
Epoch: 137 -> Test Accuracy: 91.52
[138, 60] loss: 0.011
[138, 120] loss: 0.011
[138, 180] loss: 0.011
[138, 240] loss: 0.010
[138, 300] loss: 0.011
[138, 360] loss: 0.010
Epoch: 138 -> Loss: 0.00844640098512
Epoch: 138 -> Test Accuracy: 91.52
[139, 60] loss: 0.010
[139, 120] loss: 0.010
[139, 180] loss: 0.011
[139, 240] loss: 0.011
[139, 300] loss: 0.011
[139, 360] loss: 0.010
Epoch: 139 -> Loss: 0.0280493143946
Epoch: 139 -> Test Accuracy: 91.36
[140, 60] loss: 0.011
[140, 120] loss: 0.010
[140, 180] loss: 0.012
[140, 240] loss: 0.011
[140, 300] loss: 0.012
[140, 360] loss: 0.011
Epoch: 140 -> Loss: 0.00766073446721
Epoch: 140 -> Test Accuracy: 91.4
[141, 60] loss: 0.010
[141, 120] loss: 0.010
[141, 180] loss: 0.010
[141, 240] loss: 0.011
[141, 300] loss: 0.011
[141, 360] loss: 0.010
Epoch: 141 -> Loss: 0.0104625821114
Epoch: 141 -> Test Accuracy: 91.31
[142, 60] loss: 0.011
[142, 120] loss: 0.009
[142, 180] loss: 0.010
[142, 240] loss: 0.012
[142, 300] loss: 0.009
[142, 360] loss: 0.011
Epoch: 142 -> Loss: 0.00761454086751
Epoch: 142 -> Test Accuracy: 91.44
[143, 60] loss: 0.010
[143, 120] loss: 0.009
[143, 180] loss: 0.010
[143, 240] loss: 0.010
[143, 300] loss: 0.011
[143, 360] loss: 0.010
Epoch: 143 -> Loss: 0.0193831622601
Epoch: 143 -> Test Accuracy: 91.42
[144, 60] loss: 0.011
[144, 120] loss: 0.010
[144, 180] loss: 0.009
[144, 240] loss: 0.009
[144, 300] loss: 0.010
[144, 360] loss: 0.009
Epoch: 144 -> Loss: 0.00670862803236
Epoch: 144 -> Test Accuracy: 91.32
[145, 60] loss: 0.010
[145, 120] loss: 0.010
[145, 180] loss: 0.009
[145, 240] loss: 0.010
[145, 300] loss: 0.011
[145, 360] loss: 0.011
Epoch: 145 -> Loss: 0.0103610632941
Epoch: 145 -> Test Accuracy: 91.4
[146, 60] loss: 0.010
[146, 120] loss: 0.009
[146, 180] loss: 0.009
[146, 240] loss: 0.010
[146, 300] loss: 0.009
[146, 360] loss: 0.009
Epoch: 146 -> Loss: 0.00978915672749
Epoch: 146 -> Test Accuracy: 91.5
[147, 60] loss: 0.009
[147, 120] loss: 0.010
[147, 180] loss: 0.009
[147, 240] loss: 0.009
[147, 300] loss: 0.010
[147, 360] loss: 0.009
Epoch: 147 -> Loss: 0.0133479358628
Epoch: 147 -> Test Accuracy: 91.39
[148, 60] loss: 0.009
[148, 120] loss: 0.009
[148, 180] loss: 0.009
[148, 240] loss: 0.009
[148, 300] loss: 0.008
[148, 360] loss: 0.009
Epoch: 148 -> Loss: 0.0146847125143
Epoch: 148 -> Test Accuracy: 91.49
[149, 60] loss: 0.008
[149, 120] loss: 0.009
[149, 180] loss: 0.010
[149, 240] loss: 0.009
[149, 300] loss: 0.009
[149, 360] loss: 0.009
Epoch: 149 -> Loss: 0.0182967539877
Epoch: 149 -> Test Accuracy: 91.29
[150, 60] loss: 0.009
[150, 120] loss: 0.009
[150, 180] loss: 0.008
[150, 240] loss: 0.008
[150, 300] loss: 0.009
[150, 360] loss: 0.010
Epoch: 150 -> Loss: 0.0112588824704
Epoch: 150 -> Test Accuracy: 91.47
[151, 60] loss: 0.010
[151, 120] loss: 0.009
[151, 180] loss: 0.009
[151, 240] loss: 0.009
[151, 300] loss: 0.009
[151, 360] loss: 0.009
Epoch: 151 -> Loss: 0.00795558094978
Epoch: 151 -> Test Accuracy: 91.39
[152, 60] loss: 0.009
[152, 120] loss: 0.009
[152, 180] loss: 0.008
[152, 240] loss: 0.008
[152, 300] loss: 0.009
[152, 360] loss: 0.009
Epoch: 152 -> Loss: 0.00826269388199
Epoch: 152 -> Test Accuracy: 91.09
[153, 60] loss: 0.009
[153, 120] loss: 0.010
[153, 180] loss: 0.009
[153, 240] loss: 0.008
[153, 300] loss: 0.009
[153, 360] loss: 0.009
Epoch: 153 -> Loss: 0.0122916577384
Epoch: 153 -> Test Accuracy: 91.09
[154, 60] loss: 0.009
[154, 120] loss: 0.008
[154, 180] loss: 0.008
[154, 240] loss: 0.008
[154, 300] loss: 0.009
[154, 360] loss: 0.009
Epoch: 154 -> Loss: 0.0158297717571
Epoch: 154 -> Test Accuracy: 91.09
[155, 60] loss: 0.008
[155, 120] loss: 0.009
[155, 180] loss: 0.010
[155, 240] loss: 0.009
[155, 300] loss: 0.009
[155, 360] loss: 0.009
Epoch: 155 -> Loss: 0.0062848450616
Epoch: 155 -> Test Accuracy: 91.14
[156, 60] loss: 0.009
[156, 120] loss: 0.008
[156, 180] loss: 0.009
[156, 240] loss: 0.009
[156, 300] loss: 0.009
[156, 360] loss: 0.009
Epoch: 156 -> Loss: 0.00874519906938
Epoch: 156 -> Test Accuracy: 91.34
[157, 60] loss: 0.009
[157, 120] loss: 0.008
[157, 180] loss: 0.009
[157, 240] loss: 0.008
[157, 300] loss: 0.009
[157, 360] loss: 0.008
Epoch: 157 -> Loss: 0.0130544928834
Epoch: 157 -> Test Accuracy: 91.1
[158, 60] loss: 0.008
[158, 120] loss: 0.009
[158, 180] loss: 0.008
[158, 240] loss: 0.009
[158, 300] loss: 0.008
[158, 360] loss: 0.009
Epoch: 158 -> Loss: 0.0168238095939
Epoch: 158 -> Test Accuracy: 91.31
[159, 60] loss: 0.009
[159, 120] loss: 0.008
[159, 180] loss: 0.008
[159, 240] loss: 0.008
[159, 300] loss: 0.008
[159, 360] loss: 0.010
Epoch: 159 -> Loss: 0.00392565131187
Epoch: 159 -> Test Accuracy: 91.2
[160, 60] loss: 0.008
[160, 120] loss: 0.008
[160, 180] loss: 0.008
[160, 240] loss: 0.008
[160, 300] loss: 0.009
[160, 360] loss: 0.008
Epoch: 160 -> Loss: 0.00465139141306
Epoch: 160 -> Test Accuracy: 91.41
[161, 60] loss: 0.007
[161, 120] loss: 0.007
[161, 180] loss: 0.007
[161, 240] loss: 0.008
[161, 300] loss: 0.008
[161, 360] loss: 0.007
Epoch: 161 -> Loss: 0.00661711674184
Epoch: 161 -> Test Accuracy: 91.22
[162, 60] loss: 0.008
[162, 120] loss: 0.007
[162, 180] loss: 0.007
[162, 240] loss: 0.007
[162, 300] loss: 0.007
[162, 360] loss: 0.007
Epoch: 162 -> Loss: 0.00278571853414
Epoch: 162 -> Test Accuracy: 91.34
[163, 60] loss: 0.007
[163, 120] loss: 0.007
[163, 180] loss: 0.007
[163, 240] loss: 0.008
[163, 300] loss: 0.007
[163, 360] loss: 0.007
Epoch: 163 -> Loss: 0.012463092804
Epoch: 163 -> Test Accuracy: 91.39
[164, 60] loss: 0.006
[164, 120] loss: 0.007
[164, 180] loss: 0.007
[164, 240] loss: 0.006
[164, 300] loss: 0.007
[164, 360] loss: 0.007
Epoch: 164 -> Loss: 0.03215944767
Epoch: 164 -> Test Accuracy: 91.28
[165, 60] loss: 0.007
[165, 120] loss: 0.006
[165, 180] loss: 0.007
[165, 240] loss: 0.006
[165, 300] loss: 0.007
[165, 360] loss: 0.007
Epoch: 165 -> Loss: 0.0114584919065
Epoch: 165 -> Test Accuracy: 91.31
[166, 60] loss: 0.007
[166, 120] loss: 0.006
[166, 180] loss: 0.006
[166, 240] loss: 0.007
[166, 300] loss: 0.007
[166, 360] loss: 0.007
Epoch: 166 -> Loss: 0.00599935650826
Epoch: 166 -> Test Accuracy: 91.39
[167, 60] loss: 0.007
[167, 120] loss: 0.006
[167, 180] loss: 0.007
[167, 240] loss: 0.006
[167, 300] loss: 0.006
[167, 360] loss: 0.007
Epoch: 167 -> Loss: 0.00732953567058
Epoch: 167 -> Test Accuracy: 91.35
[168, 60] loss: 0.006
[168, 120] loss: 0.007
[168, 180] loss: 0.006
[168, 240] loss: 0.006
[168, 300] loss: 0.006
[168, 360] loss: 0.007
Epoch: 168 -> Loss: 0.0101614054292
Epoch: 168 -> Test Accuracy: 91.38
[169, 60] loss: 0.007
[169, 120] loss: 0.006
[169, 180] loss: 0.006
[169, 240] loss: 0.006
[169, 300] loss: 0.007
[169, 360] loss: 0.007
Epoch: 169 -> Loss: 0.0114369038492
Epoch: 169 -> Test Accuracy: 91.44
[170, 60] loss: 0.007
[170, 120] loss: 0.007
[170, 180] loss: 0.007
[170, 240] loss: 0.007
[170, 300] loss: 0.007
[170, 360] loss: 0.007
Epoch: 170 -> Loss: 0.0217247419059
Epoch: 170 -> Test Accuracy: 91.27
[171, 60] loss: 0.007
[171, 120] loss: 0.006
[171, 180] loss: 0.006
[171, 240] loss: 0.007
[171, 300] loss: 0.007
[171, 360] loss: 0.007
Epoch: 171 -> Loss: 0.00443744659424
Epoch: 171 -> Test Accuracy: 91.35
[172, 60] loss: 0.006
[172, 120] loss: 0.007
[172, 180] loss: 0.006
[172, 240] loss: 0.007
[172, 300] loss: 0.006
[172, 360] loss: 0.006
Epoch: 172 -> Loss: 0.0275138020515
Epoch: 172 -> Test Accuracy: 91.45
[173, 60] loss: 0.006
[173, 120] loss: 0.007
[173, 180] loss: 0.006
[173, 240] loss: 0.006
[173, 300] loss: 0.006
[173, 360] loss: 0.006
Epoch: 173 -> Loss: 0.00900883041322
Epoch: 173 -> Test Accuracy: 91.31
[174, 60] loss: 0.006
[174, 120] loss: 0.007
[174, 180] loss: 0.006
[174, 240] loss: 0.006
[174, 300] loss: 0.007
[174, 360] loss: 0.007
Epoch: 174 -> Loss: 0.00365487346426
Epoch: 174 -> Test Accuracy: 91.32
[175, 60] loss: 0.006
[175, 120] loss: 0.006
[175, 180] loss: 0.006
[175, 240] loss: 0.007
[175, 300] loss: 0.006
[175, 360] loss: 0.006
Epoch: 175 -> Loss: 0.00441812258214
Epoch: 175 -> Test Accuracy: 91.28
[176, 60] loss: 0.006
[176, 120] loss: 0.007
[176, 180] loss: 0.006
[176, 240] loss: 0.006
[176, 300] loss: 0.006
[176, 360] loss: 0.006
Epoch: 176 -> Loss: 0.00915683526546
Epoch: 176 -> Test Accuracy: 91.42
[177, 60] loss: 0.007
[177, 120] loss: 0.007
[177, 180] loss: 0.006
[177, 240] loss: 0.006
[177, 300] loss: 0.007
[177, 360] loss: 0.007
Epoch: 177 -> Loss: 0.00952087063342
Epoch: 177 -> Test Accuracy: 91.3
[178, 60] loss: 0.007
[178, 120] loss: 0.006
[178, 180] loss: 0.006
[178, 240] loss: 0.007
[178, 300] loss: 0.007
[178, 360] loss: 0.007
Epoch: 178 -> Loss: 0.00619597453624
Epoch: 178 -> Test Accuracy: 91.36
[179, 60] loss: 0.006
[179, 120] loss: 0.006
[179, 180] loss: 0.006
[179, 240] loss: 0.007
[179, 300] loss: 0.007
[179, 360] loss: 0.007
Epoch: 179 -> Loss: 0.0256325509399
Epoch: 179 -> Test Accuracy: 91.39
[180, 60] loss: 0.007
[180, 120] loss: 0.006
[180, 180] loss: 0.007
[180, 240] loss: 0.007
[180, 300] loss: 0.006
[180, 360] loss: 0.007
Epoch: 180 -> Loss: 0.015832144767
Epoch: 180 -> Test Accuracy: 91.37
[181, 60] loss: 0.006
[181, 120] loss: 0.007
[181, 180] loss: 0.006
[181, 240] loss: 0.007
[181, 300] loss: 0.006
[181, 360] loss: 0.006
Epoch: 181 -> Loss: 0.00802031159401
Epoch: 181 -> Test Accuracy: 91.45
[182, 60] loss: 0.006
[182, 120] loss: 0.006
[182, 180] loss: 0.007
[182, 240] loss: 0.007
[182, 300] loss: 0.007
[182, 360] loss: 0.006
Epoch: 182 -> Loss: 0.00346255302429
Epoch: 182 -> Test Accuracy: 91.33
[183, 60] loss: 0.007
[183, 120] loss: 0.006
[183, 180] loss: 0.007
[183, 240] loss: 0.006
[183, 300] loss: 0.007
[183, 360] loss: 0.007
Epoch: 183 -> Loss: 0.00269183516502
Epoch: 183 -> Test Accuracy: 91.29
[184, 60] loss: 0.006
[184, 120] loss: 0.007
[184, 180] loss: 0.007
[184, 240] loss: 0.006
[184, 300] loss: 0.006
[184, 360] loss: 0.007
Epoch: 184 -> Loss: 0.00428304076195
Epoch: 184 -> Test Accuracy: 91.36
[185, 60] loss: 0.006
[185, 120] loss: 0.006
[185, 180] loss: 0.006
[185, 240] loss: 0.005
[185, 300] loss: 0.007
[185, 360] loss: 0.006
Epoch: 185 -> Loss: 0.0199624300003
Epoch: 185 -> Test Accuracy: 91.39
[186, 60] loss: 0.006
[186, 120] loss: 0.007
[186, 180] loss: 0.006
[186, 240] loss: 0.007
[186, 300] loss: 0.006
[186, 360] loss: 0.007
Epoch: 186 -> Loss: 0.00353973498568
Epoch: 186 -> Test Accuracy: 91.43
[187, 60] loss: 0.006
[187, 120] loss: 0.007
[187, 180] loss: 0.007
[187, 240] loss: 0.006
[187, 300] loss: 0.006
[187, 360] loss: 0.006
Epoch: 187 -> Loss: 0.00540779810399
Epoch: 187 -> Test Accuracy: 91.32
[188, 60] loss: 0.006
[188, 120] loss: 0.006
[188, 180] loss: 0.006
[188, 240] loss: 0.007
[188, 300] loss: 0.006
[188, 360] loss: 0.006
Epoch: 188 -> Loss: 0.0149178653955
Epoch: 188 -> Test Accuracy: 91.22
[189, 60] loss: 0.007
[189, 120] loss: 0.006
[189, 180] loss: 0.006
[189, 240] loss: 0.006
[189, 300] loss: 0.007
[189, 360] loss: 0.006
Epoch: 189 -> Loss: 0.00651356577873
Epoch: 189 -> Test Accuracy: 91.27
[190, 60] loss: 0.006
[190, 120] loss: 0.007
[190, 180] loss: 0.006
[190, 240] loss: 0.006
[190, 300] loss: 0.006
[190, 360] loss: 0.007
Epoch: 190 -> Loss: 0.00353185529821
Epoch: 190 -> Test Accuracy: 91.45
[191, 60] loss: 0.006
[191, 120] loss: 0.006
[191, 180] loss: 0.006
[191, 240] loss: 0.006
[191, 300] loss: 0.006
[191, 360] loss: 0.006
Epoch: 191 -> Loss: 0.00702169537544
Epoch: 191 -> Test Accuracy: 91.41
[192, 60] loss: 0.006
[192, 120] loss: 0.006
[192, 180] loss: 0.006
[192, 240] loss: 0.007
[192, 300] loss: 0.006
[192, 360] loss: 0.006
Epoch: 192 -> Loss: 0.00991275347769
Epoch: 192 -> Test Accuracy: 91.21
[193, 60] loss: 0.006
[193, 120] loss: 0.006
[193, 180] loss: 0.007
[193, 240] loss: 0.006
[193, 300] loss: 0.006
[193, 360] loss: 0.006
Epoch: 193 -> Loss: 0.00395756959915
Epoch: 193 -> Test Accuracy: 91.26
[194, 60] loss: 0.006
[194, 120] loss: 0.006
[194, 180] loss: 0.005
[194, 240] loss: 0.007
[194, 300] loss: 0.006
[194, 360] loss: 0.006
Epoch: 194 -> Loss: 0.00577930826694
Epoch: 194 -> Test Accuracy: 91.35
[195, 60] loss: 0.006
[195, 120] loss: 0.006
[195, 180] loss: 0.006
[195, 240] loss: 0.007
[195, 300] loss: 0.006
[195, 360] loss: 0.007
Epoch: 195 -> Loss: 0.00489818444476
Epoch: 195 -> Test Accuracy: 91.38
[196, 60] loss: 0.006
[196, 120] loss: 0.006
[196, 180] loss: 0.006
[196, 240] loss: 0.006
[196, 300] loss: 0.006
[196, 360] loss: 0.006
Epoch: 196 -> Loss: 0.0073150456883
Epoch: 196 -> Test Accuracy: 91.33
[197, 60] loss: 0.006
[197, 120] loss: 0.007
[197, 180] loss: 0.006
[197, 240] loss: 0.006
[197, 300] loss: 0.006
[197, 360] loss: 0.006
Epoch: 197 -> Loss: 0.017292862758
Epoch: 197 -> Test Accuracy: 91.25
[198, 60] loss: 0.006
[198, 120] loss: 0.006
[198, 180] loss: 0.006
[198, 240] loss: 0.006
[198, 300] loss: 0.006
[198, 360] loss: 0.007
Epoch: 198 -> Loss: 0.00433322181925
Epoch: 198 -> Test Accuracy: 91.31
[199, 60] loss: 0.006
[199, 120] loss: 0.006
[199, 180] loss: 0.006
[199, 240] loss: 0.006
[199, 300] loss: 0.006
[199, 360] loss: 0.006
Epoch: 199 -> Loss: 0.00762056699023
Epoch: 199 -> Test Accuracy: 91.29
[200, 60] loss: 0.006
[200, 120] loss: 0.006
[200, 180] loss: 0.007
[200, 240] loss: 0.006
[200, 300] loss: 0.006
[200, 360] loss: 0.007
Epoch: 200 -> Loss: 0.0176921673119
Epoch: 200 -> Test Accuracy: 91.39
Finished Training
In [11]:
# save variables
fm.save_variable([class_NIN_loss_log, class_NIN_test_accuracy_log], "supervised_NIN")

Semi-supervised Learning

In [12]:
# initialize networks
semi_net = fm.load_net("RotNet_rotation_200_4_block_net")
In [13]:
semi_loss_log, semi_accuracy_log, super_loss_log, super_accuracy_log = tr.train_semi([20, 100, 400, 1000, 5000], 10, 
    trainset, testset, 128, [0.1, 0.02, 0.004, 0.0008], [35, 70, 85, 100], [0.1, 0.02, 0.004, 0.0008],
               [60, 120, 160, 200], 0.9, 5e-4, semi_net, criterion)
Epoch: 1 -> Loss: 2.27038741112
Epoch: 1 -> Test Accuracy: 38.2
Epoch: 2 -> Loss: 1.25083875656
Epoch: 2 -> Test Accuracy: 50.25
Epoch: 3 -> Loss: 1.05511724949
Epoch: 3 -> Test Accuracy: 54.67
Epoch: 4 -> Loss: 0.768583297729
Epoch: 4 -> Test Accuracy: 55.78
Epoch: 5 -> Loss: 0.580367803574
Epoch: 5 -> Test Accuracy: 57.47
Epoch: 6 -> Loss: 0.346341788769
Epoch: 6 -> Test Accuracy: 58.57
Epoch: 7 -> Loss: 0.391008704901
Epoch: 7 -> Test Accuracy: 58.75
Epoch: 8 -> Loss: 0.21460570395
Epoch: 8 -> Test Accuracy: 59.13
Epoch: 9 -> Loss: 0.178526058793
Epoch: 9 -> Test Accuracy: 59.64
Epoch: 10 -> Loss: 0.193584755063
Epoch: 10 -> Test Accuracy: 59.73
Epoch: 11 -> Loss: 0.0977763086557
Epoch: 11 -> Test Accuracy: 59.9
Epoch: 12 -> Loss: 0.104682013392
Epoch: 12 -> Test Accuracy: 60.15
Epoch: 13 -> Loss: 0.0651761591434
Epoch: 13 -> Test Accuracy: 60.38
Epoch: 14 -> Loss: 0.0543492771685
Epoch: 14 -> Test Accuracy: 60.51
Epoch: 15 -> Loss: 0.0612868741155
Epoch: 15 -> Test Accuracy: 60.52
Epoch: 16 -> Loss: 0.042023614049
Epoch: 16 -> Test Accuracy: 60.71
Epoch: 17 -> Loss: 0.0215672515333
Epoch: 17 -> Test Accuracy: 61.07
Epoch: 18 -> Loss: 0.0256984103471
Epoch: 18 -> Test Accuracy: 61.27
Epoch: 19 -> Loss: 0.0214232727885
Epoch: 19 -> Test Accuracy: 61.38
Epoch: 20 -> Loss: 0.0318058468401
Epoch: 20 -> Test Accuracy: 61.63
Epoch: 21 -> Loss: 0.0183569863439
Epoch: 21 -> Test Accuracy: 61.75
Epoch: 22 -> Loss: 0.0253742858768
Epoch: 22 -> Test Accuracy: 61.72
Epoch: 23 -> Loss: 0.0246881116182
Epoch: 23 -> Test Accuracy: 61.89
Epoch: 24 -> Loss: 0.017155315727
Epoch: 24 -> Test Accuracy: 61.95
Epoch: 25 -> Loss: 0.0272710844874
Epoch: 25 -> Test Accuracy: 62.08
Epoch: 26 -> Loss: 0.0113559830934
Epoch: 26 -> Test Accuracy: 62.15
Epoch: 27 -> Loss: 0.0115368366241
Epoch: 27 -> Test Accuracy: 62.23
Epoch: 28 -> Loss: 0.00796966440976
Epoch: 28 -> Test Accuracy: 62.23
Epoch: 29 -> Loss: 0.0120957894251
Epoch: 29 -> Test Accuracy: 62.25
Epoch: 30 -> Loss: 0.0114964116365
Epoch: 30 -> Test Accuracy: 62.24
Epoch: 31 -> Loss: 0.0101838968694
Epoch: 31 -> Test Accuracy: 62.28
Epoch: 32 -> Loss: 0.01213702932
Epoch: 32 -> Test Accuracy: 62.43
Epoch: 33 -> Loss: 0.0101211536676
Epoch: 33 -> Test Accuracy: 62.46
Epoch: 34 -> Loss: 0.010621256195
Epoch: 34 -> Test Accuracy: 62.56
Epoch: 35 -> Loss: 0.00744563341141
Epoch: 35 -> Test Accuracy: 62.55
Epoch: 36 -> Loss: 0.0056139761582
Epoch: 36 -> Test Accuracy: 62.55
Epoch: 37 -> Loss: 0.00848679430783
Epoch: 37 -> Test Accuracy: 62.56
Epoch: 38 -> Loss: 0.00818242598325
Epoch: 38 -> Test Accuracy: 62.57
Epoch: 39 -> Loss: 0.00871019251645
Epoch: 39 -> Test Accuracy: 62.55
Epoch: 40 -> Loss: 0.00552598619834
Epoch: 40 -> Test Accuracy: 62.58
Epoch: 41 -> Loss: 0.00760336732492
Epoch: 41 -> Test Accuracy: 62.6
Epoch: 42 -> Loss: 0.00530742947012
Epoch: 42 -> Test Accuracy: 62.61
Epoch: 43 -> Loss: 0.00672916555777
Epoch: 43 -> Test Accuracy: 62.63
Epoch: 44 -> Loss: 0.00878243334591
Epoch: 44 -> Test Accuracy: 62.59
Epoch: 45 -> Loss: 0.00568009726703
Epoch: 45 -> Test Accuracy: 62.62
Epoch: 46 -> Loss: 0.0154432523996
Epoch: 46 -> Test Accuracy: 62.62
Epoch: 47 -> Loss: 0.00832791440189
Epoch: 47 -> Test Accuracy: 62.68
Epoch: 48 -> Loss: 0.00655911350623
Epoch: 48 -> Test Accuracy: 62.73
Epoch: 49 -> Loss: 0.00694914674386
Epoch: 49 -> Test Accuracy: 62.71
Epoch: 50 -> Loss: 0.00769170792773
Epoch: 50 -> Test Accuracy: 62.77
Epoch: 51 -> Loss: 0.00710220448673
Epoch: 51 -> Test Accuracy: 62.73
Epoch: 52 -> Loss: 0.00946570094675
Epoch: 52 -> Test Accuracy: 62.66
Epoch: 53 -> Loss: 0.0101848579943
Epoch: 53 -> Test Accuracy: 62.63
Epoch: 54 -> Loss: 0.0074348449707
Epoch: 54 -> Test Accuracy: 62.65
Epoch: 55 -> Loss: 0.00517143821344
Epoch: 55 -> Test Accuracy: 62.62
Epoch: 56 -> Loss: 0.00575730530545
Epoch: 56 -> Test Accuracy: 62.59
Epoch: 57 -> Loss: 0.00401691580191
Epoch: 57 -> Test Accuracy: 62.6
Epoch: 58 -> Loss: 0.00543261226267
Epoch: 58 -> Test Accuracy: 62.62
Epoch: 59 -> Loss: 0.0074658789672
Epoch: 59 -> Test Accuracy: 62.62
Epoch: 60 -> Loss: 0.00945476070046
Epoch: 60 -> Test Accuracy: 62.6
Epoch: 61 -> Loss: 0.00629272079095
Epoch: 61 -> Test Accuracy: 62.58
Epoch: 62 -> Loss: 0.00855879019946
Epoch: 62 -> Test Accuracy: 62.62
Epoch: 63 -> Loss: 0.00633266242221
Epoch: 63 -> Test Accuracy: 62.62
Epoch: 64 -> Loss: 0.010481165722
Epoch: 64 -> Test Accuracy: 62.62
Epoch: 65 -> Loss: 0.00626732921228
Epoch: 65 -> Test Accuracy: 62.64
Epoch: 66 -> Loss: 0.00693629868329
Epoch: 66 -> Test Accuracy: 62.66
Epoch: 67 -> Loss: 0.00575800053775
Epoch: 67 -> Test Accuracy: 62.76
Epoch: 68 -> Loss: 0.00760897016153
Epoch: 68 -> Test Accuracy: 62.69
Epoch: 69 -> Loss: 0.00582766532898
Epoch: 69 -> Test Accuracy: 62.71
Epoch: 70 -> Loss: 0.00553533760831
Epoch: 70 -> Test Accuracy: 62.71
Epoch: 71 -> Loss: 0.00661115534604
Epoch: 71 -> Test Accuracy: 62.73
Epoch: 72 -> Loss: 0.00559503491968
Epoch: 72 -> Test Accuracy: 62.72
Epoch: 73 -> Loss: 0.00652698008344
Epoch: 73 -> Test Accuracy: 62.72
Epoch: 74 -> Loss: 0.00753558333963
Epoch: 74 -> Test Accuracy: 62.72
Epoch: 75 -> Loss: 0.00685798469931
Epoch: 75 -> Test Accuracy: 62.73
Epoch: 76 -> Loss: 0.00662627490237
Epoch: 76 -> Test Accuracy: 62.71
Epoch: 77 -> Loss: 0.00527127599344
Epoch: 77 -> Test Accuracy: 62.71
Epoch: 78 -> Loss: 0.00652544386685
Epoch: 78 -> Test Accuracy: 62.75
Epoch: 79 -> Loss: 0.00536925252527
Epoch: 79 -> Test Accuracy: 62.73
Epoch: 80 -> Loss: 0.0083562200889
Epoch: 80 -> Test Accuracy: 62.74
Epoch: 81 -> Loss: 0.00584895722568
Epoch: 81 -> Test Accuracy: 62.75
Epoch: 82 -> Loss: 0.0057873330079
Epoch: 82 -> Test Accuracy: 62.76
Epoch: 83 -> Loss: 0.00544299697503
Epoch: 83 -> Test Accuracy: 62.78
Epoch: 84 -> Loss: 0.00688730366528
Epoch: 84 -> Test Accuracy: 62.78
Epoch: 85 -> Loss: 0.00633131805807
Epoch: 85 -> Test Accuracy: 62.79
Epoch: 86 -> Loss: 0.0042668976821
Epoch: 86 -> Test Accuracy: 62.79
Epoch: 87 -> Loss: 0.00515410630032
Epoch: 87 -> Test Accuracy: 62.79
Epoch: 88 -> Loss: 0.00569624360651
Epoch: 88 -> Test Accuracy: 62.79
Epoch: 89 -> Loss: 0.0118077462539
Epoch: 89 -> Test Accuracy: 62.79
Epoch: 90 -> Loss: 0.00667405128479
Epoch: 90 -> Test Accuracy: 62.78
Epoch: 91 -> Loss: 0.00953802838922
Epoch: 91 -> Test Accuracy: 62.78
Epoch: 92 -> Loss: 0.00702718878165
Epoch: 92 -> Test Accuracy: 62.78
Epoch: 93 -> Loss: 0.00599869759753
Epoch: 93 -> Test Accuracy: 62.78
Epoch: 94 -> Loss: 0.00567687861621
Epoch: 94 -> Test Accuracy: 62.78
Epoch: 95 -> Loss: 0.00786071363837
Epoch: 95 -> Test Accuracy: 62.78
Epoch: 96 -> Loss: 0.00724229542539
Epoch: 96 -> Test Accuracy: 62.77
Epoch: 97 -> Loss: 0.00592624489218
Epoch: 97 -> Test Accuracy: 62.76
Epoch: 98 -> Loss: 0.00631262874231
Epoch: 98 -> Test Accuracy: 62.76
Epoch: 99 -> Loss: 0.00812559667975
Epoch: 99 -> Test Accuracy: 62.76
Epoch: 100 -> Loss: 0.00592105928808
Epoch: 100 -> Test Accuracy: 62.76
Finished Training
Epoch: 1 -> Loss: 2.65849399567
Epoch: 1 -> Test Accuracy: 17.76
Epoch: 2 -> Loss: 2.65423417091
Epoch: 2 -> Test Accuracy: 19.05
Epoch: 3 -> Loss: 2.54765224457
Epoch: 3 -> Test Accuracy: 21.98
Epoch: 4 -> Loss: 1.95757567883
Epoch: 4 -> Test Accuracy: 23.75
Epoch: 5 -> Loss: 1.69368875027
Epoch: 5 -> Test Accuracy: 25.06
Epoch: 6 -> Loss: 1.53716754913
Epoch: 6 -> Test Accuracy: 26.19
Epoch: 7 -> Loss: 1.46634602547
Epoch: 7 -> Test Accuracy: 28.46
Epoch: 8 -> Loss: 1.33804786205
Epoch: 8 -> Test Accuracy: 29.75
Epoch: 9 -> Loss: 1.22867488861
Epoch: 9 -> Test Accuracy: 29.07
Epoch: 10 -> Loss: 1.29244184494
Epoch: 10 -> Test Accuracy: 30.62
Epoch: 11 -> Loss: 1.11101794243
Epoch: 11 -> Test Accuracy: 29.46
Epoch: 12 -> Loss: 0.977714300156
Epoch: 12 -> Test Accuracy: 30.08
Epoch: 13 -> Loss: 0.839048564434
Epoch: 13 -> Test Accuracy: 30.15
Epoch: 14 -> Loss: 0.728604733944
Epoch: 14 -> Test Accuracy: 30.15
Epoch: 15 -> Loss: 0.652540504932
Epoch: 15 -> Test Accuracy: 28.31
Epoch: 16 -> Loss: 0.60499227047
Epoch: 16 -> Test Accuracy: 29.02
Epoch: 17 -> Loss: 0.671340584755
Epoch: 17 -> Test Accuracy: 29.49
Epoch: 18 -> Loss: 0.610807180405
Epoch: 18 -> Test Accuracy: 31.09
Epoch: 19 -> Loss: 0.481778770685
Epoch: 19 -> Test Accuracy: 30.48
Epoch: 20 -> Loss: 0.695512056351
Epoch: 20 -> Test Accuracy: 28.5
Epoch: 21 -> Loss: 0.49528503418
Epoch: 21 -> Test Accuracy: 29.71
Epoch: 22 -> Loss: 0.508057355881
Epoch: 22 -> Test Accuracy: 29.87
Epoch: 23 -> Loss: 0.484232485294
Epoch: 23 -> Test Accuracy: 30.31
Epoch: 24 -> Loss: 0.409639388323
Epoch: 24 -> Test Accuracy: 29.3
Epoch: 25 -> Loss: 0.400426089764
Epoch: 25 -> Test Accuracy: 30.4
Epoch: 26 -> Loss: 0.366675168276
Epoch: 26 -> Test Accuracy: 31.84
Epoch: 27 -> Loss: 0.213309317827
Epoch: 27 -> Test Accuracy: 29.36
Epoch: 28 -> Loss: 0.190486699343
Epoch: 28 -> Test Accuracy: 30.37
Epoch: 29 -> Loss: 0.183573544025
Epoch: 29 -> Test Accuracy: 30.41
Epoch: 30 -> Loss: 0.121843934059
Epoch: 30 -> Test Accuracy: 30.46
Epoch: 31 -> Loss: 0.0818746984005
Epoch: 31 -> Test Accuracy: 30.43
Epoch: 32 -> Loss: 0.0889547541738
Epoch: 32 -> Test Accuracy: 30.57
Epoch: 33 -> Loss: 0.146276921034
Epoch: 33 -> Test Accuracy: 30.31
Epoch: 34 -> Loss: 0.12046982348
Epoch: 34 -> Test Accuracy: 30.44
Epoch: 35 -> Loss: 0.0818599760532
Epoch: 35 -> Test Accuracy: 29.31
Epoch: 36 -> Loss: 0.093117967248
Epoch: 36 -> Test Accuracy: 31.31
Epoch: 37 -> Loss: 0.108391337097
Epoch: 37 -> Test Accuracy: 31.04
Epoch: 38 -> Loss: 0.225953370333
Epoch: 38 -> Test Accuracy: 30.36
Epoch: 39 -> Loss: 0.0714892223477
Epoch: 39 -> Test Accuracy: 30.66
Epoch: 40 -> Loss: 0.102141693234
Epoch: 40 -> Test Accuracy: 31.18
Epoch: 41 -> Loss: 0.0641302764416
Epoch: 41 -> Test Accuracy: 31.13
Epoch: 42 -> Loss: 0.0632255971432
Epoch: 42 -> Test Accuracy: 31.5
Epoch: 43 -> Loss: 0.058499738574
Epoch: 43 -> Test Accuracy: 31.91
Epoch: 44 -> Loss: 0.0465745925903
Epoch: 44 -> Test Accuracy: 32.22
Epoch: 45 -> Loss: 0.06388682127
Epoch: 45 -> Test Accuracy: 31.56
Epoch: 46 -> Loss: 0.0645600110292
Epoch: 46 -> Test Accuracy: 30.91
Epoch: 47 -> Loss: 0.0624152608216
Epoch: 47 -> Test Accuracy: 30.72
Epoch: 48 -> Loss: 0.0933792591095
Epoch: 48 -> Test Accuracy: 30.18
Epoch: 49 -> Loss: 0.0777014568448
Epoch: 49 -> Test Accuracy: 29.82
Epoch: 50 -> Loss: 0.079465046525
Epoch: 50 -> Test Accuracy: 29.38
Epoch: 51 -> Loss: 0.0368623062968
Epoch: 51 -> Test Accuracy: 30.6
Epoch: 52 -> Loss: 0.0495228022337
Epoch: 52 -> Test Accuracy: 30.79
Epoch: 53 -> Loss: 0.0355994179845
Epoch: 53 -> Test Accuracy: 30.73
Epoch: 54 -> Loss: 0.0513694621623
Epoch: 54 -> Test Accuracy: 30.46
Epoch: 55 -> Loss: 0.0200139954686
Epoch: 55 -> Test Accuracy: 30.79
Epoch: 56 -> Loss: 0.0263384841383
Epoch: 56 -> Test Accuracy: 30.83
Epoch: 57 -> Loss: 0.0260647013783
Epoch: 57 -> Test Accuracy: 30.98
Epoch: 58 -> Loss: 0.013524711132
Epoch: 58 -> Test Accuracy: 31.37
Epoch: 59 -> Loss: 0.0266250167042
Epoch: 59 -> Test Accuracy: 31.43
Epoch: 60 -> Loss: 0.0162335038185
Epoch: 60 -> Test Accuracy: 30.95
Epoch: 61 -> Loss: 0.030623389408
Epoch: 61 -> Test Accuracy: 31.17
Epoch: 62 -> Loss: 0.0176982078701
Epoch: 62 -> Test Accuracy: 31.21
Epoch: 63 -> Loss: 0.0143640972674
Epoch: 63 -> Test Accuracy: 31.37
Epoch: 64 -> Loss: 0.0239562653005
Epoch: 64 -> Test Accuracy: 31.48
Epoch: 65 -> Loss: 0.00683768605813
Epoch: 65 -> Test Accuracy: 31.25
Epoch: 66 -> Loss: 0.0121848322451
Epoch: 66 -> Test Accuracy: 31.28
Epoch: 67 -> Loss: 0.00535400724038
Epoch: 67 -> Test Accuracy: 31.24
Epoch: 68 -> Loss: 0.0268093682826
Epoch: 68 -> Test Accuracy: 31.1
Epoch: 69 -> Loss: 0.0117152733728
Epoch: 69 -> Test Accuracy: 31.16
Epoch: 70 -> Loss: 0.014351089485
Epoch: 70 -> Test Accuracy: 31.23
Epoch: 71 -> Loss: 0.0194191131741
Epoch: 71 -> Test Accuracy: 31.08
Epoch: 72 -> Loss: 0.0089210011065
Epoch: 72 -> Test Accuracy: 31.08
Epoch: 73 -> Loss: 0.00735072977841
Epoch: 73 -> Test Accuracy: 31.08
Epoch: 74 -> Loss: 0.00625147437677
Epoch: 74 -> Test Accuracy: 31.25
Epoch: 75 -> Loss: 0.00578457769006
Epoch: 75 -> Test Accuracy: 31.31
Epoch: 76 -> Loss: 0.00498634576797
Epoch: 76 -> Test Accuracy: 31.34
Epoch: 77 -> Loss: 0.00985836330801
Epoch: 77 -> Test Accuracy: 31.35
Epoch: 78 -> Loss: 0.0651436969638
Epoch: 78 -> Test Accuracy: 31.62
Epoch: 79 -> Loss: 0.0210603680462
Epoch: 79 -> Test Accuracy: 31.77
Epoch: 80 -> Loss: 0.0499172620475
Epoch: 80 -> Test Accuracy: 31.56
Epoch: 81 -> Loss: 0.00692547671497
Epoch: 81 -> Test Accuracy: 31.42
Epoch: 82 -> Loss: 0.00814078934491
Epoch: 82 -> Test Accuracy: 31.31
Epoch: 83 -> Loss: 0.0120227206498
Epoch: 83 -> Test Accuracy: 31.35
Epoch: 84 -> Loss: 0.00890287384391
Epoch: 84 -> Test Accuracy: 31.36
Epoch: 85 -> Loss: 0.00359779596329
Epoch: 85 -> Test Accuracy: 31.45
Epoch: 86 -> Loss: 0.00684562651441
Epoch: 86 -> Test Accuracy: 31.4
Epoch: 87 -> Loss: 0.0163849070668
Epoch: 87 -> Test Accuracy: 31.4
Epoch: 88 -> Loss: 0.00557531928644
Epoch: 88 -> Test Accuracy: 31.25
Epoch: 89 -> Loss: 0.0057560140267
Epoch: 89 -> Test Accuracy: 31.26
Epoch: 90 -> Loss: 0.0173290707171
Epoch: 90 -> Test Accuracy: 31.32
Epoch: 91 -> Loss: 0.00566754071042
Epoch: 91 -> Test Accuracy: 31.4
Epoch: 92 -> Loss: 0.00838792324066
Epoch: 92 -> Test Accuracy: 31.53
Epoch: 93 -> Loss: 0.00608476670459
Epoch: 93 -> Test Accuracy: 31.63
Epoch: 94 -> Loss: 0.00523587083444
Epoch: 94 -> Test Accuracy: 31.67
Epoch: 95 -> Loss: 0.00402182340622
Epoch: 95 -> Test Accuracy: 31.78
Epoch: 96 -> Loss: 0.00507264677435
Epoch: 96 -> Test Accuracy: 31.74
Epoch: 97 -> Loss: 0.012475874275
Epoch: 97 -> Test Accuracy: 31.77
Epoch: 98 -> Loss: 0.0043402579613
Epoch: 98 -> Test Accuracy: 31.85
Epoch: 99 -> Loss: 0.00990788824856
Epoch: 99 -> Test Accuracy: 31.88
Epoch: 100 -> Loss: 0.00575581518933
Epoch: 100 -> Test Accuracy: 31.85
Epoch: 101 -> Loss: 0.0046511227265
Epoch: 101 -> Test Accuracy: 31.94
Epoch: 102 -> Loss: 0.00604559993371
Epoch: 102 -> Test Accuracy: 31.93
Epoch: 103 -> Loss: 0.00441769743338
Epoch: 103 -> Test Accuracy: 31.89
Epoch: 104 -> Loss: 0.0139822624624
Epoch: 104 -> Test Accuracy: 31.81
Epoch: 105 -> Loss: 0.00459103472531
Epoch: 105 -> Test Accuracy: 31.66
Epoch: 106 -> Loss: 0.00472034327686
Epoch: 106 -> Test Accuracy: 31.53
Epoch: 107 -> Loss: 0.00645211013034
Epoch: 107 -> Test Accuracy: 31.45
Epoch: 108 -> Loss: 0.00555882183835
Epoch: 108 -> Test Accuracy: 31.4
Epoch: 109 -> Loss: 0.0086893774569
Epoch: 109 -> Test Accuracy: 31.34
Epoch: 110 -> Loss: 0.00391525682062
Epoch: 110 -> Test Accuracy: 31.29
Epoch: 111 -> Loss: 0.00485305674374
Epoch: 111 -> Test Accuracy: 31.16
Epoch: 112 -> Loss: 0.0193780455738
Epoch: 112 -> Test Accuracy: 31.42
Epoch: 113 -> Loss: 0.00531618483365
Epoch: 113 -> Test Accuracy: 31.51
Epoch: 114 -> Loss: 0.0139916278422
Epoch: 114 -> Test Accuracy: 31.66
Epoch: 115 -> Loss: 0.00473481416702
Epoch: 115 -> Test Accuracy: 31.7
Epoch: 116 -> Loss: 0.00936312135309
Epoch: 116 -> Test Accuracy: 31.6
Epoch: 117 -> Loss: 0.00522234709933
Epoch: 117 -> Test Accuracy: 31.6
Epoch: 118 -> Loss: 0.00459918053821
Epoch: 118 -> Test Accuracy: 31.64
Epoch: 119 -> Loss: 0.0116134220734
Epoch: 119 -> Test Accuracy: 31.65
Epoch: 120 -> Loss: 0.00426397053525
Epoch: 120 -> Test Accuracy: 31.66
Epoch: 121 -> Loss: 0.00583248678595
Epoch: 121 -> Test Accuracy: 31.65
Epoch: 122 -> Loss: 0.00510450219736
Epoch: 122 -> Test Accuracy: 31.68
Epoch: 123 -> Loss: 0.0061276354827
Epoch: 123 -> Test Accuracy: 31.66
Epoch: 124 -> Loss: 0.0043531190604
Epoch: 124 -> Test Accuracy: 31.64
Epoch: 125 -> Loss: 0.00450514443219
Epoch: 125 -> Test Accuracy: 31.64
Epoch: 126 -> Loss: 0.00457972288132
Epoch: 126 -> Test Accuracy: 31.67
Epoch: 127 -> Loss: 0.00374668836594
Epoch: 127 -> Test Accuracy: 31.65
Epoch: 128 -> Loss: 0.00424822187051
Epoch: 128 -> Test Accuracy: 31.67
Epoch: 129 -> Loss: 0.00375601975247
Epoch: 129 -> Test Accuracy: 31.63
Epoch: 130 -> Loss: 0.0044202208519
Epoch: 130 -> Test Accuracy: 31.63
Epoch: 131 -> Loss: 0.0126273762435
Epoch: 131 -> Test Accuracy: 31.57
Epoch: 132 -> Loss: 0.00593539746478
Epoch: 132 -> Test Accuracy: 31.6
Epoch: 133 -> Loss: 0.00738661829382
Epoch: 133 -> Test Accuracy: 31.61
Epoch: 134 -> Loss: 0.00854841899127
Epoch: 134 -> Test Accuracy: 31.63
Epoch: 135 -> Loss: 0.00512178055942
Epoch: 135 -> Test Accuracy: 31.59
Epoch: 136 -> Loss: 0.00753069575876
Epoch: 136 -> Test Accuracy: 31.58
Epoch: 137 -> Loss: 0.00834982283413
Epoch: 137 -> Test Accuracy: 31.63
Epoch: 138 -> Loss: 0.0110670793802
Epoch: 138 -> Test Accuracy: 31.67
Epoch: 139 -> Loss: 0.00301223341376
Epoch: 139 -> Test Accuracy: 31.66
Epoch: 140 -> Loss: 0.00494349002838
Epoch: 140 -> Test Accuracy: 31.69
Epoch: 141 -> Loss: 0.0113283265382
Epoch: 141 -> Test Accuracy: 31.69
Epoch: 142 -> Loss: 0.0196752622724
Epoch: 142 -> Test Accuracy: 31.72
Epoch: 143 -> Loss: 0.00411151535809
Epoch: 143 -> Test Accuracy: 31.7
Epoch: 144 -> Loss: 0.00513248331845
Epoch: 144 -> Test Accuracy: 31.63
Epoch: 145 -> Loss: 0.00871815253049
Epoch: 145 -> Test Accuracy: 31.67
Epoch: 146 -> Loss: 0.00703046703711
Epoch: 146 -> Test Accuracy: 31.62
Epoch: 147 -> Loss: 0.00342211453244
Epoch: 147 -> Test Accuracy: 31.58
Epoch: 148 -> Loss: 0.00384040013887
Epoch: 148 -> Test Accuracy: 31.61
Epoch: 149 -> Loss: 0.00589236291125
Epoch: 149 -> Test Accuracy: 31.64
Epoch: 150 -> Loss: 0.00738715473562
Epoch: 150 -> Test Accuracy: 31.62
Epoch: 151 -> Loss: 0.00435251649469
Epoch: 151 -> Test Accuracy: 31.61
Epoch: 152 -> Loss: 0.00312617095187
Epoch: 152 -> Test Accuracy: 31.65
Epoch: 153 -> Loss: 0.00460541248322
Epoch: 153 -> Test Accuracy: 31.67
Epoch: 154 -> Loss: 0.00909231137484
Epoch: 154 -> Test Accuracy: 31.7
Epoch: 155 -> Loss: 0.00584716256708
Epoch: 155 -> Test Accuracy: 31.7
Epoch: 156 -> Loss: 0.0074987676926
Epoch: 156 -> Test Accuracy: 31.64
Epoch: 157 -> Loss: 0.0039552715607
Epoch: 157 -> Test Accuracy: 31.6
Epoch: 158 -> Loss: 0.00470164744183
Epoch: 158 -> Test Accuracy: 31.57
Epoch: 159 -> Loss: 0.00638218037784
Epoch: 159 -> Test Accuracy: 31.58
Epoch: 160 -> Loss: 0.00485002342612
Epoch: 160 -> Test Accuracy: 31.58
Epoch: 161 -> Loss: 0.00382403540425
Epoch: 161 -> Test Accuracy: 31.57
Epoch: 162 -> Loss: 0.00521539989859
Epoch: 162 -> Test Accuracy: 31.58
Epoch: 163 -> Loss: 0.0062480699271
Epoch: 163 -> Test Accuracy: 31.58
Epoch: 164 -> Loss: 0.0029730333481
Epoch: 164 -> Test Accuracy: 31.59
Epoch: 165 -> Loss: 0.0035161243286
Epoch: 165 -> Test Accuracy: 31.59
Epoch: 166 -> Loss: 0.00549176009372
Epoch: 166 -> Test Accuracy: 31.59
Epoch: 167 -> Loss: 0.00459265056998
Epoch: 167 -> Test Accuracy: 31.6
Epoch: 168 -> Loss: 0.00541173107922
Epoch: 168 -> Test Accuracy: 31.61
Epoch: 169 -> Loss: 0.00387814315036
Epoch: 169 -> Test Accuracy: 31.62
Epoch: 170 -> Loss: 0.00673397397622
Epoch: 170 -> Test Accuracy: 31.63
Epoch: 171 -> Loss: 0.00420149834827
Epoch: 171 -> Test Accuracy: 31.64
Epoch: 172 -> Loss: 0.00555738480762
Epoch: 172 -> Test Accuracy: 31.63
Epoch: 173 -> Loss: 0.00288179190829
Epoch: 173 -> Test Accuracy: 31.62
Epoch: 174 -> Loss: 0.00811217259616
Epoch: 174 -> Test Accuracy: 31.62
Epoch: 175 -> Loss: 0.00299417972565
Epoch: 175 -> Test Accuracy: 31.62
Epoch: 176 -> Loss: 0.00542287016287
Epoch: 176 -> Test Accuracy: 31.61
Epoch: 177 -> Loss: 0.00397737137973
Epoch: 177 -> Test Accuracy: 31.61
Epoch: 178 -> Loss: 0.00658184289932
Epoch: 178 -> Test Accuracy: 31.6
Epoch: 179 -> Loss: 0.00398606061935
Epoch: 179 -> Test Accuracy: 31.6
Epoch: 180 -> Loss: 0.00338594126515
Epoch: 180 -> Test Accuracy: 31.59
Epoch: 181 -> Loss: 0.00717590935528
Epoch: 181 -> Test Accuracy: 31.58
Epoch: 182 -> Loss: 0.00511586666107
Epoch: 182 -> Test Accuracy: 31.59
Epoch: 183 -> Loss: 0.00559444539249
Epoch: 183 -> Test Accuracy: 31.59
Epoch: 184 -> Loss: 0.00833480246365
Epoch: 184 -> Test Accuracy: 31.61
Epoch: 185 -> Loss: 0.00428612343967
Epoch: 185 -> Test Accuracy: 31.61
Epoch: 186 -> Loss: 0.00440870411694
Epoch: 186 -> Test Accuracy: 31.6
Epoch: 187 -> Loss: 0.00514138396829
Epoch: 187 -> Test Accuracy: 31.61
Epoch: 188 -> Loss: 0.00491127697751
Epoch: 188 -> Test Accuracy: 31.6
Epoch: 189 -> Loss: 0.00435345713049
Epoch: 189 -> Test Accuracy: 31.59
Epoch: 190 -> Loss: 0.00580557854846
Epoch: 190 -> Test Accuracy: 31.58
Epoch: 191 -> Loss: 0.00609395233914
Epoch: 191 -> Test Accuracy: 31.58
Epoch: 192 -> Loss: 0.0024350087624
Epoch: 192 -> Test Accuracy: 31.59
Epoch: 193 -> Loss: 0.00519629986957
Epoch: 193 -> Test Accuracy: 31.6
Epoch: 194 -> Loss: 0.00413072761148
Epoch: 194 -> Test Accuracy: 31.59
Epoch: 195 -> Loss: 0.0133555531502
Epoch: 195 -> Test Accuracy: 31.58
Epoch: 196 -> Loss: 0.00494430446997
Epoch: 196 -> Test Accuracy: 31.57
Epoch: 197 -> Loss: 0.00487032858655
Epoch: 197 -> Test Accuracy: 31.56
Epoch: 198 -> Loss: 0.00334726436995
Epoch: 198 -> Test Accuracy: 31.56
Epoch: 199 -> Loss: 0.00479398155585
Epoch: 199 -> Test Accuracy: 31.57
Epoch: 200 -> Loss: 0.00458396784961
Epoch: 200 -> Test Accuracy: 31.57
Finished Training
Epoch: 1 -> Loss: 1.16978991032
Epoch: 1 -> Test Accuracy: 59.11
Epoch: 2 -> Loss: 0.818692564964
Epoch: 2 -> Test Accuracy: 64.85
Epoch: 3 -> Loss: 0.515661180019
Epoch: 3 -> Test Accuracy: 67.52
Epoch: 4 -> Loss: 0.498400062323
Epoch: 4 -> Test Accuracy: 69.39
Epoch: 5 -> Loss: 0.295192241669
Epoch: 5 -> Test Accuracy: 69.17
Epoch: 6 -> Loss: 0.346483647823
Epoch: 6 -> Test Accuracy: 70.04
Epoch: 7 -> Loss: 0.35073107481
Epoch: 7 -> Test Accuracy: 69.81
Epoch: 8 -> Loss: 0.164541393518
Epoch: 8 -> Test Accuracy: 70.21
Epoch: 9 -> Loss: 0.136162385345
Epoch: 9 -> Test Accuracy: 70.43
Epoch: 10 -> Loss: 0.197705596685
Epoch: 10 -> Test Accuracy: 69.83
Epoch: 11 -> Loss: 0.161909133196
Epoch: 11 -> Test Accuracy: 71.09
Epoch: 12 -> Loss: 0.128920659423
Epoch: 12 -> Test Accuracy: 70.28
Epoch: 13 -> Loss: 0.141596734524
Epoch: 13 -> Test Accuracy: 69.96
Epoch: 14 -> Loss: 0.0781158283353
Epoch: 14 -> Test Accuracy: 70.14
Epoch: 15 -> Loss: 0.0786408931017
Epoch: 15 -> Test Accuracy: 70.51
Epoch: 16 -> Loss: 0.0920756980777
Epoch: 16 -> Test Accuracy: 70.42
Epoch: 17 -> Loss: 0.0319330617785
Epoch: 17 -> Test Accuracy: 69.73
Epoch: 18 -> Loss: 0.0327526777983
Epoch: 18 -> Test Accuracy: 70.93
Epoch: 19 -> Loss: 0.0480217300355
Epoch: 19 -> Test Accuracy: 71.4
Epoch: 20 -> Loss: 0.0420212186873
Epoch: 20 -> Test Accuracy: 71.05
Epoch: 21 -> Loss: 0.0309940446168
Epoch: 21 -> Test Accuracy: 71.16
Epoch: 22 -> Loss: 0.0445150509477
Epoch: 22 -> Test Accuracy: 71.39
Epoch: 23 -> Loss: 0.023487329483
Epoch: 23 -> Test Accuracy: 70.6
Epoch: 24 -> Loss: 0.0216508414596
Epoch: 24 -> Test Accuracy: 71.47
Epoch: 25 -> Loss: 0.0365014225245
Epoch: 25 -> Test Accuracy: 71.49
Epoch: 26 -> Loss: 0.0115071814507
Epoch: 26 -> Test Accuracy: 70.8
Epoch: 27 -> Loss: 0.0269990563393
Epoch: 27 -> Test Accuracy: 71.03
Epoch: 28 -> Loss: 0.0126689812168
Epoch: 28 -> Test Accuracy: 71.18
Epoch: 29 -> Loss: 0.0354838557541
Epoch: 29 -> Test Accuracy: 71.09
Epoch: 30 -> Loss: 0.02070748806
Epoch: 30 -> Test Accuracy: 71.47
Epoch: 31 -> Loss: 0.0218499582261
Epoch: 31 -> Test Accuracy: 71.19
Epoch: 32 -> Loss: 0.0233822092414
Epoch: 32 -> Test Accuracy: 70.63
Epoch: 33 -> Loss: 0.0201046839356
Epoch: 33 -> Test Accuracy: 71.1
Epoch: 34 -> Loss: 0.0154700558633
Epoch: 34 -> Test Accuracy: 71.22
Epoch: 35 -> Loss: 0.0124344872311
Epoch: 35 -> Test Accuracy: 71.47
Epoch: 36 -> Loss: 0.015577564016
Epoch: 36 -> Test Accuracy: 71.46
Epoch: 37 -> Loss: 0.0123796053231
Epoch: 37 -> Test Accuracy: 71.68
Epoch: 38 -> Loss: 0.013300373219
Epoch: 38 -> Test Accuracy: 71.61
Epoch: 39 -> Loss: 0.0090951602906
Epoch: 39 -> Test Accuracy: 71.66
Epoch: 40 -> Loss: 0.0186039805412
Epoch: 40 -> Test Accuracy: 71.73
Epoch: 41 -> Loss: 0.00844493694603
Epoch: 41 -> Test Accuracy: 71.96
Epoch: 42 -> Loss: 0.00948825664818
Epoch: 42 -> Test Accuracy: 71.94
Epoch: 43 -> Loss: 0.00697893369943
Epoch: 43 -> Test Accuracy: 71.9
Epoch: 44 -> Loss: 0.00895012356341
Epoch: 44 -> Test Accuracy: 71.79
Epoch: 45 -> Loss: 0.00774023169652
Epoch: 45 -> Test Accuracy: 71.84
Epoch: 46 -> Loss: 0.00836301781237
Epoch: 46 -> Test Accuracy: 71.84
Epoch: 47 -> Loss: 0.0102208806202
Epoch: 47 -> Test Accuracy: 71.81
Epoch: 48 -> Loss: 0.00655163265765
Epoch: 48 -> Test Accuracy: 71.85
Epoch: 49 -> Loss: 0.0093550728634
Epoch: 49 -> Test Accuracy: 71.73
Epoch: 50 -> Loss: 0.00735352607444
Epoch: 50 -> Test Accuracy: 71.74
Epoch: 51 -> Loss: 0.0107001615688
Epoch: 51 -> Test Accuracy: 71.73
Epoch: 52 -> Loss: 0.00513489451259
Epoch: 52 -> Test Accuracy: 71.81
Epoch: 53 -> Loss: 0.018765360117
Epoch: 53 -> Test Accuracy: 71.87
Epoch: 54 -> Loss: 0.012613048777
Epoch: 54 -> Test Accuracy: 71.83
Epoch: 55 -> Loss: 0.0124139096588
Epoch: 55 -> Test Accuracy: 71.87
Epoch: 56 -> Loss: 0.0081590320915
Epoch: 56 -> Test Accuracy: 71.87
Epoch: 57 -> Loss: 0.00564740272239
Epoch: 57 -> Test Accuracy: 71.85
Epoch: 58 -> Loss: 0.00776688428596
Epoch: 58 -> Test Accuracy: 71.85
Epoch: 59 -> Loss: 0.00804035924375
Epoch: 59 -> Test Accuracy: 71.88
Epoch: 60 -> Loss: 0.00311387493275
Epoch: 60 -> Test Accuracy: 71.94
Epoch: 61 -> Loss: 0.00572899216786
Epoch: 61 -> Test Accuracy: 71.97
Epoch: 62 -> Loss: 0.0060246726498
Epoch: 62 -> Test Accuracy: 71.96
Epoch: 63 -> Loss: 0.00840062834322
Epoch: 63 -> Test Accuracy: 71.86
Epoch: 64 -> Loss: 0.00872503314167
Epoch: 64 -> Test Accuracy: 71.85
Epoch: 65 -> Loss: 0.00926504191011
Epoch: 65 -> Test Accuracy: 71.82
Epoch: 66 -> Loss: 0.00849655456841
Epoch: 66 -> Test Accuracy: 71.89
Epoch: 67 -> Loss: 0.00573908817023
Epoch: 67 -> Test Accuracy: 71.89
Epoch: 68 -> Loss: 0.00538792507723
Epoch: 68 -> Test Accuracy: 71.85
Epoch: 69 -> Loss: 0.005356724374
Epoch: 69 -> Test Accuracy: 71.9
Epoch: 70 -> Loss: 0.00815541017801
Epoch: 70 -> Test Accuracy: 71.84
Epoch: 71 -> Loss: 0.00879112072289
Epoch: 71 -> Test Accuracy: 71.86
Epoch: 72 -> Loss: 0.00691547291353
Epoch: 72 -> Test Accuracy: 71.88
Epoch: 73 -> Loss: 0.00516853434965
Epoch: 73 -> Test Accuracy: 71.91
Epoch: 74 -> Loss: 0.00770607823506
Epoch: 74 -> Test Accuracy: 71.88
Epoch: 75 -> Loss: 0.00592618249357
Epoch: 75 -> Test Accuracy: 71.91
Epoch: 76 -> Loss: 0.00764277810231
Epoch: 76 -> Test Accuracy: 71.88
Epoch: 77 -> Loss: 0.00649404991418
Epoch: 77 -> Test Accuracy: 71.91
Epoch: 78 -> Loss: 0.00508999358863
Epoch: 78 -> Test Accuracy: 71.91
Epoch: 79 -> Loss: 0.00620726915076
Epoch: 79 -> Test Accuracy: 71.9
Epoch: 80 -> Loss: 0.00717619759962
Epoch: 80 -> Test Accuracy: 71.86
Epoch: 81 -> Loss: 0.00679866177961
Epoch: 81 -> Test Accuracy: 71.82
Epoch: 82 -> Loss: 0.00567138195038
Epoch: 82 -> Test Accuracy: 71.82
Epoch: 83 -> Loss: 0.00546513171867
Epoch: 83 -> Test Accuracy: 71.84
Epoch: 84 -> Loss: 0.00826396420598
Epoch: 84 -> Test Accuracy: 71.79
Epoch: 85 -> Loss: 0.00735905067995
Epoch: 85 -> Test Accuracy: 71.81
Epoch: 86 -> Loss: 0.00670008454472
Epoch: 86 -> Test Accuracy: 71.82
Epoch: 87 -> Loss: 0.00763354869559
Epoch: 87 -> Test Accuracy: 71.82
Epoch: 88 -> Loss: 0.0086984038353
Epoch: 88 -> Test Accuracy: 71.83
Epoch: 89 -> Loss: 0.0146390432492
Epoch: 89 -> Test Accuracy: 71.83
Epoch: 90 -> Loss: 0.00588022731245
Epoch: 90 -> Test Accuracy: 71.83
Epoch: 91 -> Loss: 0.00926686264575
Epoch: 91 -> Test Accuracy: 71.82
Epoch: 92 -> Loss: 0.00477366242558
Epoch: 92 -> Test Accuracy: 71.81
Epoch: 93 -> Loss: 0.0100494027138
Epoch: 93 -> Test Accuracy: 71.79
Epoch: 94 -> Loss: 0.00503945350647
Epoch: 94 -> Test Accuracy: 71.79
Epoch: 95 -> Loss: 0.00737430946901
Epoch: 95 -> Test Accuracy: 71.79
Epoch: 96 -> Loss: 0.00864776782691
Epoch: 96 -> Test Accuracy: 71.79
Epoch: 97 -> Loss: 0.00645641656592
Epoch: 97 -> Test Accuracy: 71.8
Epoch: 98 -> Loss: 0.00669449102134
Epoch: 98 -> Test Accuracy: 71.81
Epoch: 99 -> Loss: 0.00832581054419
Epoch: 99 -> Test Accuracy: 71.82
Epoch: 100 -> Loss: 0.00613514287397
Epoch: 100 -> Test Accuracy: 71.78
Finished Training
Epoch: 1 -> Loss: 2.05860495567
Epoch: 1 -> Test Accuracy: 25.15
Epoch: 2 -> Loss: 1.78287065029
Epoch: 2 -> Test Accuracy: 31.69
Epoch: 3 -> Loss: 1.66563344002
Epoch: 3 -> Test Accuracy: 34.04
Epoch: 4 -> Loss: 1.66787862778
Epoch: 4 -> Test Accuracy: 35.87
Epoch: 5 -> Loss: 1.75792908669
Epoch: 5 -> Test Accuracy: 36.5
Epoch: 6 -> Loss: 1.48490762711
Epoch: 6 -> Test Accuracy: 39.22
Epoch: 7 -> Loss: 1.53739726543
Epoch: 7 -> Test Accuracy: 39.08
Epoch: 8 -> Loss: 1.43894505501
Epoch: 8 -> Test Accuracy: 37.4
Epoch: 9 -> Loss: 1.30448019505
Epoch: 9 -> Test Accuracy: 40.94
Epoch: 10 -> Loss: 1.24514913559
Epoch: 10 -> Test Accuracy: 41.34
Epoch: 11 -> Loss: 1.22006881237
Epoch: 11 -> Test Accuracy: 40.87
Epoch: 12 -> Loss: 1.12812423706
Epoch: 12 -> Test Accuracy: 39.79
Epoch: 13 -> Loss: 1.05018496513
Epoch: 13 -> Test Accuracy: 39.84
Epoch: 14 -> Loss: 0.94600135088
Epoch: 14 -> Test Accuracy: 39.32
Epoch: 15 -> Loss: 0.998095750809
Epoch: 15 -> Test Accuracy: 41.34
Epoch: 16 -> Loss: 0.936528921127
Epoch: 16 -> Test Accuracy: 42.1
Epoch: 17 -> Loss: 0.833999037743
Epoch: 17 -> Test Accuracy: 42.84
Epoch: 18 -> Loss: 1.10956180096
Epoch: 18 -> Test Accuracy: 39.31
Epoch: 19 -> Loss: 1.00225949287
Epoch: 19 -> Test Accuracy: 41.07
Epoch: 20 -> Loss: 0.925218343735
Epoch: 20 -> Test Accuracy: 40.87
Epoch: 21 -> Loss: 0.835498392582
Epoch: 21 -> Test Accuracy: 39.0
Epoch: 22 -> Loss: 0.811240792274
Epoch: 22 -> Test Accuracy: 41.49
Epoch: 23 -> Loss: 0.677923440933
Epoch: 23 -> Test Accuracy: 42.51
Epoch: 24 -> Loss: 0.590499579906
Epoch: 24 -> Test Accuracy: 41.46
Epoch: 25 -> Loss: 0.656732559204
Epoch: 25 -> Test Accuracy: 42.96
Epoch: 26 -> Loss: 0.519568741322
Epoch: 26 -> Test Accuracy: 41.32
Epoch: 27 -> Loss: 0.569069862366
Epoch: 27 -> Test Accuracy: 42.16
Epoch: 28 -> Loss: 0.688864827156
Epoch: 28 -> Test Accuracy: 42.76
Epoch: 29 -> Loss: 0.537516713142
Epoch: 29 -> Test Accuracy: 41.28
Epoch: 30 -> Loss: 0.490972310305
Epoch: 30 -> Test Accuracy: 42.4
Epoch: 31 -> Loss: 0.443497300148
Epoch: 31 -> Test Accuracy: 39.4
Epoch: 32 -> Loss: 0.480938374996
Epoch: 32 -> Test Accuracy: 41.85
Epoch: 33 -> Loss: 0.395873755217
Epoch: 33 -> Test Accuracy: 41.77
Epoch: 34 -> Loss: 0.296258032322
Epoch: 34 -> Test Accuracy: 43.45
Epoch: 35 -> Loss: 0.30421307683
Epoch: 35 -> Test Accuracy: 42.5
Epoch: 36 -> Loss: 0.182013511658
Epoch: 36 -> Test Accuracy: 43.09
Epoch: 37 -> Loss: 0.299805849791
Epoch: 37 -> Test Accuracy: 42.3
Epoch: 38 -> Loss: 0.338209331036
Epoch: 38 -> Test Accuracy: 42.69
Epoch: 39 -> Loss: 0.215267896652
Epoch: 39 -> Test Accuracy: 42.66
Epoch: 40 -> Loss: 0.149372398853
Epoch: 40 -> Test Accuracy: 43.64
Epoch: 41 -> Loss: 0.0939838811755
Epoch: 41 -> Test Accuracy: 43.16
Epoch: 42 -> Loss: 0.212134540081
Epoch: 42 -> Test Accuracy: 42.59
Epoch: 43 -> Loss: 0.258519947529
Epoch: 43 -> Test Accuracy: 43.65
Epoch: 44 -> Loss: 0.104442670941
Epoch: 44 -> Test Accuracy: 43.66
Epoch: 45 -> Loss: 0.179340541363
Epoch: 45 -> Test Accuracy: 44.88
Epoch: 46 -> Loss: 0.251038402319
Epoch: 46 -> Test Accuracy: 44.42
Epoch: 47 -> Loss: 0.157367676497
Epoch: 47 -> Test Accuracy: 43.56
Epoch: 48 -> Loss: 0.168719470501
Epoch: 48 -> Test Accuracy: 44.92
Epoch: 49 -> Loss: 0.167285218835
Epoch: 49 -> Test Accuracy: 43.99
Epoch: 50 -> Loss: 0.0926831290126
Epoch: 50 -> Test Accuracy: 44.11
Epoch: 51 -> Loss: 0.137827664614
Epoch: 51 -> Test Accuracy: 43.54
Epoch: 52 -> Loss: 0.240762472153
Epoch: 52 -> Test Accuracy: 41.44
Epoch: 53 -> Loss: 0.144982472062
Epoch: 53 -> Test Accuracy: 43.09
Epoch: 54 -> Loss: 0.18570753932
Epoch: 54 -> Test Accuracy: 42.68
Epoch: 55 -> Loss: 0.192132502794
Epoch: 55 -> Test Accuracy: 43.06
Epoch: 56 -> Loss: 0.258671820164
Epoch: 56 -> Test Accuracy: 42.77
Epoch: 57 -> Loss: 0.30471265316
Epoch: 57 -> Test Accuracy: 42.62
Epoch: 58 -> Loss: 0.239147558808
Epoch: 58 -> Test Accuracy: 44.32
Epoch: 59 -> Loss: 0.123470336199
Epoch: 59 -> Test Accuracy: 42.78
Epoch: 60 -> Loss: 0.101262368262
Epoch: 60 -> Test Accuracy: 44.99
Epoch: 61 -> Loss: 0.0398100018501
Epoch: 61 -> Test Accuracy: 46.09
Epoch: 62 -> Loss: 0.0573702082038
Epoch: 62 -> Test Accuracy: 46.69
Epoch: 63 -> Loss: 0.0128948185593
Epoch: 63 -> Test Accuracy: 46.87
Epoch: 64 -> Loss: 0.0159148797393
Epoch: 64 -> Test Accuracy: 46.61
Epoch: 65 -> Loss: 0.0188232883811
Epoch: 65 -> Test Accuracy: 47.03
Epoch: 66 -> Loss: 0.00894800480455
Epoch: 66 -> Test Accuracy: 47.06
Epoch: 67 -> Loss: 0.0125405974686
Epoch: 67 -> Test Accuracy: 46.83
Epoch: 68 -> Loss: 0.0152934240177
Epoch: 68 -> Test Accuracy: 46.97
Epoch: 69 -> Loss: 0.0110622961074
Epoch: 69 -> Test Accuracy: 47.0
Epoch: 70 -> Loss: 0.0274295527488
Epoch: 70 -> Test Accuracy: 47.0
Epoch: 71 -> Loss: 0.0108768204227
Epoch: 71 -> Test Accuracy: 47.14
Epoch: 72 -> Loss: 0.0234214700758
Epoch: 72 -> Test Accuracy: 47.18
Epoch: 73 -> Loss: 0.0140837933868
Epoch: 73 -> Test Accuracy: 46.94
Epoch: 74 -> Loss: 0.0126457260922
Epoch: 74 -> Test Accuracy: 46.98
Epoch: 75 -> Loss: 0.00654622679576
Epoch: 75 -> Test Accuracy: 47.04
Epoch: 76 -> Loss: 0.020926296711
Epoch: 76 -> Test Accuracy: 47.03
Epoch: 77 -> Loss: 0.00728975329548
Epoch: 77 -> Test Accuracy: 47.06
Epoch: 78 -> Loss: 0.0109080672264
Epoch: 78 -> Test Accuracy: 46.94
Epoch: 79 -> Loss: 0.00699828239158
Epoch: 79 -> Test Accuracy: 46.85
Epoch: 80 -> Loss: 0.00645909877494
Epoch: 80 -> Test Accuracy: 46.87
Epoch: 81 -> Loss: 0.0141588449478
Epoch: 81 -> Test Accuracy: 47.0
Epoch: 82 -> Loss: 0.00822214409709
Epoch: 82 -> Test Accuracy: 47.06
Epoch: 83 -> Loss: 0.0105800908059
Epoch: 83 -> Test Accuracy: 46.97
Epoch: 84 -> Loss: 0.00409800745547
Epoch: 84 -> Test Accuracy: 46.82
Epoch: 85 -> Loss: 0.00754146371037
Epoch: 85 -> Test Accuracy: 46.87
Epoch: 86 -> Loss: 0.00601873034611
Epoch: 86 -> Test Accuracy: 47.0
Epoch: 87 -> Loss: 0.00431439979002
Epoch: 87 -> Test Accuracy: 46.96
Epoch: 88 -> Loss: 0.00561239616945
Epoch: 88 -> Test Accuracy: 46.82
Epoch: 89 -> Loss: 0.00743468012661
Epoch: 89 -> Test Accuracy: 46.87
Epoch: 90 -> Loss: 0.00627677235752
Epoch: 90 -> Test Accuracy: 46.92
Epoch: 91 -> Loss: 0.01095334813
Epoch: 91 -> Test Accuracy: 46.93
Epoch: 92 -> Loss: 0.00718308892101
Epoch: 92 -> Test Accuracy: 46.93
Epoch: 93 -> Loss: 0.00643192324787
Epoch: 93 -> Test Accuracy: 46.97
Epoch: 94 -> Loss: 0.00616349605843
Epoch: 94 -> Test Accuracy: 47.14
Epoch: 95 -> Loss: 0.00576082104817
Epoch: 95 -> Test Accuracy: 47.17
Epoch: 96 -> Loss: 0.00719059910625
Epoch: 96 -> Test Accuracy: 47.24
Epoch: 97 -> Loss: 0.0111631155014
Epoch: 97 -> Test Accuracy: 47.14
Epoch: 98 -> Loss: 0.00323654594831
Epoch: 98 -> Test Accuracy: 47.26
Epoch: 99 -> Loss: 0.00591270299628
Epoch: 99 -> Test Accuracy: 47.12
Epoch: 100 -> Loss: 0.00590293202549
Epoch: 100 -> Test Accuracy: 47.13
Epoch: 101 -> Loss: 0.0117588918656
Epoch: 101 -> Test Accuracy: 46.9
Epoch: 102 -> Loss: 0.00545776356012
Epoch: 102 -> Test Accuracy: 47.09
Epoch: 103 -> Loss: 0.00437755323946
Epoch: 103 -> Test Accuracy: 46.95
Epoch: 104 -> Loss: 0.00414527859539
Epoch: 104 -> Test Accuracy: 46.93
Epoch: 105 -> Loss: 0.00360571872443
Epoch: 105 -> Test Accuracy: 46.87
Epoch: 106 -> Loss: 0.00386307784356
Epoch: 106 -> Test Accuracy: 46.84
Epoch: 107 -> Loss: 0.00497916107997
Epoch: 107 -> Test Accuracy: 46.9
Epoch: 108 -> Loss: 0.0105075147003
Epoch: 108 -> Test Accuracy: 46.98
Epoch: 109 -> Loss: 0.004586242605
Epoch: 109 -> Test Accuracy: 47.06
Epoch: 110 -> Loss: 0.0039576520212
Epoch: 110 -> Test Accuracy: 47.13
Epoch: 111 -> Loss: 0.00331057491712
Epoch: 111 -> Test Accuracy: 47.13
Epoch: 112 -> Loss: 0.00532398326322
Epoch: 112 -> Test Accuracy: 47.12
Epoch: 113 -> Loss: 0.00696651311591
Epoch: 113 -> Test Accuracy: 47.12
Epoch: 114 -> Loss: 0.00535412039608
Epoch: 114 -> Test Accuracy: 47.23
Epoch: 115 -> Loss: 0.00419379677624
Epoch: 115 -> Test Accuracy: 47.04
Epoch: 116 -> Loss: 0.00531852245331
Epoch: 116 -> Test Accuracy: 47.08
Epoch: 117 -> Loss: 0.0045576277189
Epoch: 117 -> Test Accuracy: 47.05
Epoch: 118 -> Loss: 0.0185530818999
Epoch: 118 -> Test Accuracy: 47.16
Epoch: 119 -> Loss: 0.00319520779885
Epoch: 119 -> Test Accuracy: 47.24
Epoch: 120 -> Loss: 0.00451296102256
Epoch: 120 -> Test Accuracy: 47.24
Epoch: 121 -> Loss: 0.00657169613987
Epoch: 121 -> Test Accuracy: 47.2
Epoch: 122 -> Loss: 0.00609574420378
Epoch: 122 -> Test Accuracy: 47.14
Epoch: 123 -> Loss: 0.00424097152427
Epoch: 123 -> Test Accuracy: 47.2
Epoch: 124 -> Loss: 0.00957666896284
Epoch: 124 -> Test Accuracy: 47.2
Epoch: 125 -> Loss: 0.00429891142994
Epoch: 125 -> Test Accuracy: 47.23
Epoch: 126 -> Loss: 0.00412431592122
Epoch: 126 -> Test Accuracy: 47.18
Epoch: 127 -> Loss: 0.00693973666057
Epoch: 127 -> Test Accuracy: 47.19
Epoch: 128 -> Loss: 0.0054598543793
Epoch: 128 -> Test Accuracy: 47.2
Epoch: 129 -> Loss: 0.00380060309544
Epoch: 129 -> Test Accuracy: 47.25
Epoch: 130 -> Loss: 0.00369319552556
Epoch: 130 -> Test Accuracy: 47.27
Epoch: 131 -> Loss: 0.0040620197542
Epoch: 131 -> Test Accuracy: 47.29
Epoch: 132 -> Loss: 0.00732275145128
Epoch: 132 -> Test Accuracy: 47.27
Epoch: 133 -> Loss: 0.00532024633139
Epoch: 133 -> Test Accuracy: 47.29
Epoch: 134 -> Loss: 0.00293260347098
Epoch: 134 -> Test Accuracy: 47.28
Epoch: 135 -> Loss: 0.00995481014252
Epoch: 135 -> Test Accuracy: 47.24
Epoch: 136 -> Loss: 0.00302057992667
Epoch: 136 -> Test Accuracy: 47.22
Epoch: 137 -> Loss: 0.00484318006784
Epoch: 137 -> Test Accuracy: 47.22
Epoch: 138 -> Loss: 0.00447314046323
Epoch: 138 -> Test Accuracy: 47.18
Epoch: 139 -> Loss: 0.00498041743413
Epoch: 139 -> Test Accuracy: 47.11
Epoch: 140 -> Loss: 0.00438820384443
Epoch: 140 -> Test Accuracy: 47.16
Epoch: 141 -> Loss: 0.00395818380639
Epoch: 141 -> Test Accuracy: 47.22
Epoch: 142 -> Loss: 0.00632557505742
Epoch: 142 -> Test Accuracy: 47.21
Epoch: 143 -> Loss: 0.00284797861241
Epoch: 143 -> Test Accuracy: 47.2
Epoch: 144 -> Loss: 0.00460153352469
Epoch: 144 -> Test Accuracy: 47.2
Epoch: 145 -> Loss: 0.00562093826011
Epoch: 145 -> Test Accuracy: 47.19
Epoch: 146 -> Loss: 0.00456475745887
Epoch: 146 -> Test Accuracy: 47.19
Epoch: 147 -> Loss: 0.00395743642002
Epoch: 147 -> Test Accuracy: 47.17
Epoch: 148 -> Loss: 0.00426904950291
Epoch: 148 -> Test Accuracy: 47.19
Epoch: 149 -> Loss: 0.00532081490383
Epoch: 149 -> Test Accuracy: 47.17
Epoch: 150 -> Loss: 0.00454872380942
Epoch: 150 -> Test Accuracy: 47.22
Epoch: 151 -> Loss: 0.00441893702373
Epoch: 151 -> Test Accuracy: 47.25
Epoch: 152 -> Loss: 0.00605936255306
Epoch: 152 -> Test Accuracy: 47.22
Epoch: 153 -> Loss: 0.00341761577874
Epoch: 153 -> Test Accuracy: 47.21
Epoch: 154 -> Loss: 0.00391801958904
Epoch: 154 -> Test Accuracy: 47.16
Epoch: 155 -> Loss: 0.00770230498165
Epoch: 155 -> Test Accuracy: 47.17
Epoch: 156 -> Loss: 0.00276749418117
Epoch: 156 -> Test Accuracy: 47.12
Epoch: 157 -> Loss: 0.00425694091246
Epoch: 157 -> Test Accuracy: 47.13
Epoch: 158 -> Loss: 0.00491246813908
Epoch: 158 -> Test Accuracy: 47.11
Epoch: 159 -> Loss: 0.0060608247295
Epoch: 159 -> Test Accuracy: 47.11
Epoch: 160 -> Loss: 0.00407360168174
Epoch: 160 -> Test Accuracy: 47.16
Epoch: 161 -> Loss: 0.00385200069286
Epoch: 161 -> Test Accuracy: 47.16
Epoch: 162 -> Loss: 0.00673266546801
Epoch: 162 -> Test Accuracy: 47.16
Epoch: 163 -> Loss: 0.00338697899133
Epoch: 163 -> Test Accuracy: 47.15
Epoch: 164 -> Loss: 0.00463841063902
Epoch: 164 -> Test Accuracy: 47.14
Epoch: 165 -> Loss: 0.0174027401954
Epoch: 165 -> Test Accuracy: 47.14
Epoch: 166 -> Loss: 0.0067554069683
Epoch: 166 -> Test Accuracy: 47.14
Epoch: 167 -> Loss: 0.00396752357483
Epoch: 167 -> Test Accuracy: 47.19
Epoch: 168 -> Loss: 0.00519067980349
Epoch: 168 -> Test Accuracy: 47.21
Epoch: 169 -> Loss: 0.00537413358688
Epoch: 169 -> Test Accuracy: 47.2
Epoch: 170 -> Loss: 0.00553401606157
Epoch: 170 -> Test Accuracy: 47.19
Epoch: 171 -> Loss: 0.00237574032508
Epoch: 171 -> Test Accuracy: 47.17
Epoch: 172 -> Loss: 0.00498402584344
Epoch: 172 -> Test Accuracy: 47.17
Epoch: 173 -> Loss: 0.00355258351192
Epoch: 173 -> Test Accuracy: 47.16
Epoch: 174 -> Loss: 0.00436259247363
Epoch: 174 -> Test Accuracy: 47.17
Epoch: 175 -> Loss: 0.0060517648235
Epoch: 175 -> Test Accuracy: 47.19
Epoch: 176 -> Loss: 0.00835307780653
Epoch: 176 -> Test Accuracy: 47.19
Epoch: 177 -> Loss: 0.00520370109007
Epoch: 177 -> Test Accuracy: 47.19
Epoch: 178 -> Loss: 0.00470145372674
Epoch: 178 -> Test Accuracy: 47.18
Epoch: 179 -> Loss: 0.00333922193386
Epoch: 179 -> Test Accuracy: 47.17
Epoch: 180 -> Loss: 0.00964744295925
Epoch: 180 -> Test Accuracy: 47.15
Epoch: 181 -> Loss: 0.00630944035947
Epoch: 181 -> Test Accuracy: 47.13
Epoch: 182 -> Loss: 0.00370637280867
Epoch: 182 -> Test Accuracy: 47.13
Epoch: 183 -> Loss: 0.00374528532848
Epoch: 183 -> Test Accuracy: 47.1
Epoch: 184 -> Loss: 0.00446284748614
Epoch: 184 -> Test Accuracy: 47.11
Epoch: 185 -> Loss: 0.00385080859996
Epoch: 185 -> Test Accuracy: 47.1
Epoch: 186 -> Loss: 0.00440237624571
Epoch: 186 -> Test Accuracy: 47.1
Epoch: 187 -> Loss: 0.00561613310128
Epoch: 187 -> Test Accuracy: 47.1
Epoch: 188 -> Loss: 0.00740049453452
Epoch: 188 -> Test Accuracy: 47.1
Epoch: 189 -> Loss: 0.00424044858664
Epoch: 189 -> Test Accuracy: 47.11
Epoch: 190 -> Loss: 0.00532710086554
Epoch: 190 -> Test Accuracy: 47.09
Epoch: 191 -> Loss: 0.0054624080658
Epoch: 191 -> Test Accuracy: 47.1
Epoch: 192 -> Loss: 0.00476060016081
Epoch: 192 -> Test Accuracy: 47.1
Epoch: 193 -> Loss: 0.00857401359826
Epoch: 193 -> Test Accuracy: 47.13
Epoch: 194 -> Loss: 0.00691185984761
Epoch: 194 -> Test Accuracy: 47.11
Epoch: 195 -> Loss: 0.00707859732211
Epoch: 195 -> Test Accuracy: 47.11
Epoch: 196 -> Loss: 0.00377793493681
Epoch: 196 -> Test Accuracy: 47.08
Epoch: 197 -> Loss: 0.00461611384526
Epoch: 197 -> Test Accuracy: 47.09
Epoch: 198 -> Loss: 0.00951327290386
Epoch: 198 -> Test Accuracy: 47.09
Epoch: 199 -> Loss: 0.004498898983
Epoch: 199 -> Test Accuracy: 47.1
Epoch: 200 -> Loss: 0.00615973677486
Epoch: 200 -> Test Accuracy: 47.1
Finished Training
Epoch: 1 -> Loss: 0.802093267441
Epoch: 1 -> Test Accuracy: 66.99
Epoch: 2 -> Loss: 0.424829810858
Epoch: 2 -> Test Accuracy: 74.03
Epoch: 3 -> Loss: 0.504541635513
Epoch: 3 -> Test Accuracy: 75.08
Epoch: 4 -> Loss: 0.546547710896
Epoch: 4 -> Test Accuracy: 75.39
Epoch: 5 -> Loss: 0.378071367741
Epoch: 5 -> Test Accuracy: 74.59
Epoch: 6 -> Loss: 0.171422198415
Epoch: 6 -> Test Accuracy: 75.79
Epoch: 7 -> Loss: 0.351442366838
Epoch: 7 -> Test Accuracy: 76.77
Epoch: 8 -> Loss: 0.239226520061
Epoch: 8 -> Test Accuracy: 76.55
Epoch: 9 -> Loss: 0.245262667537
Epoch: 9 -> Test Accuracy: 77.22
Epoch: 10 -> Loss: 0.276399612427
Epoch: 10 -> Test Accuracy: 76.33
Epoch: 11 -> Loss: 0.186288997531
Epoch: 11 -> Test Accuracy: 77.66
Epoch: 12 -> Loss: 0.0832670927048
Epoch: 12 -> Test Accuracy: 77.25
Epoch: 13 -> Loss: 0.237387895584
Epoch: 13 -> Test Accuracy: 76.98
Epoch: 14 -> Loss: 0.196971029043
Epoch: 14 -> Test Accuracy: 77.13
Epoch: 15 -> Loss: 0.103413499892
Epoch: 15 -> Test Accuracy: 76.8
Epoch: 16 -> Loss: 0.0992646813393
Epoch: 16 -> Test Accuracy: 76.57
Epoch: 17 -> Loss: 0.226926401258
Epoch: 17 -> Test Accuracy: 77.19
Epoch: 18 -> Loss: 0.375478118658
Epoch: 18 -> Test Accuracy: 77.9
Epoch: 19 -> Loss: 0.0377269536257
Epoch: 19 -> Test Accuracy: 77.06
Epoch: 20 -> Loss: 0.0722538083792
Epoch: 20 -> Test Accuracy: 77.72
Epoch: 21 -> Loss: 0.0992808267474
Epoch: 21 -> Test Accuracy: 78.41
Epoch: 22 -> Loss: 0.0456341058016
Epoch: 22 -> Test Accuracy: 77.95
Epoch: 23 -> Loss: 0.0169827789068
Epoch: 23 -> Test Accuracy: 78.21
Epoch: 24 -> Loss: 0.155332386494
Epoch: 24 -> Test Accuracy: 77.87
Epoch: 25 -> Loss: 0.0860455930233
Epoch: 25 -> Test Accuracy: 78.49
Epoch: 26 -> Loss: 0.242450863123
Epoch: 26 -> Test Accuracy: 77.65
Epoch: 27 -> Loss: 0.0242157131433
Epoch: 27 -> Test Accuracy: 77.42
Epoch: 28 -> Loss: 0.216665014625
Epoch: 28 -> Test Accuracy: 78.57
Epoch: 29 -> Loss: 0.0592583715916
Epoch: 29 -> Test Accuracy: 78.14
Epoch: 30 -> Loss: 0.146587640047
Epoch: 30 -> Test Accuracy: 77.86
Epoch: 31 -> Loss: 0.212334707379
Epoch: 31 -> Test Accuracy: 78.53
Epoch: 32 -> Loss: 0.0875750482082
Epoch: 32 -> Test Accuracy: 77.61
Epoch: 33 -> Loss: 0.0500359088182
Epoch: 33 -> Test Accuracy: 77.65
Epoch: 34 -> Loss: 0.153146550059
Epoch: 34 -> Test Accuracy: 77.89
Epoch: 35 -> Loss: 0.0208696424961
Epoch: 35 -> Test Accuracy: 78.92
Epoch: 36 -> Loss: 0.0984496027231
Epoch: 36 -> Test Accuracy: 79.71
Epoch: 37 -> Loss: 0.0195654183626
Epoch: 37 -> Test Accuracy: 79.92
Epoch: 38 -> Loss: 0.0948273837566
Epoch: 38 -> Test Accuracy: 79.98
Epoch: 39 -> Loss: 0.0658591911197
Epoch: 39 -> Test Accuracy: 80.08
Epoch: 40 -> Loss: 0.0876868665218
Epoch: 40 -> Test Accuracy: 80.02
Epoch: 41 -> Loss: 0.0620073378086
Epoch: 41 -> Test Accuracy: 79.98
Epoch: 42 -> Loss: 0.0212653577328
Epoch: 42 -> Test Accuracy: 80.16
Epoch: 43 -> Loss: 0.0179961770773
Epoch: 43 -> Test Accuracy: 80.03
Epoch: 44 -> Loss: 0.0110031217337
Epoch: 44 -> Test Accuracy: 79.95
Epoch: 45 -> Loss: 0.00997103750706
Epoch: 45 -> Test Accuracy: 80.04
Epoch: 46 -> Loss: 0.00899240374565
Epoch: 46 -> Test Accuracy: 80.09
Epoch: 47 -> Loss: 0.0105749368668
Epoch: 47 -> Test Accuracy: 80.0
Epoch: 48 -> Loss: 0.0594830662012
Epoch: 48 -> Test Accuracy: 80.25
Epoch: 49 -> Loss: 0.00699333846569
Epoch: 49 -> Test Accuracy: 80.16
Epoch: 50 -> Loss: 0.0160281956196
Epoch: 50 -> Test Accuracy: 80.26
Epoch: 51 -> Loss: 0.0268976092339
Epoch: 51 -> Test Accuracy: 80.28
Epoch: 52 -> Loss: 0.0339367985725
Epoch: 52 -> Test Accuracy: 80.0
Epoch: 53 -> Loss: 0.0193541646004
Epoch: 53 -> Test Accuracy: 80.12
Epoch: 54 -> Loss: 0.0446498543024
Epoch: 54 -> Test Accuracy: 80.04
Epoch: 55 -> Loss: 0.0253207534552
Epoch: 55 -> Test Accuracy: 79.98
Epoch: 56 -> Loss: 0.00760178267956
Epoch: 56 -> Test Accuracy: 80.08
Epoch: 57 -> Loss: 0.116646245122
Epoch: 57 -> Test Accuracy: 80.19
Epoch: 58 -> Loss: 0.0476978570223
Epoch: 58 -> Test Accuracy: 79.99
Epoch: 59 -> Loss: 0.0428672581911
Epoch: 59 -> Test Accuracy: 79.79
Epoch: 60 -> Loss: 0.0110652148724
Epoch: 60 -> Test Accuracy: 79.96
Epoch: 61 -> Loss: 0.0361245274544
Epoch: 61 -> Test Accuracy: 80.15
Epoch: 62 -> Loss: 0.00841024518013
Epoch: 62 -> Test Accuracy: 80.16
Epoch: 63 -> Loss: 0.0088861733675
Epoch: 63 -> Test Accuracy: 80.05
Epoch: 64 -> Loss: 0.00655518472195
Epoch: 64 -> Test Accuracy: 80.16
Epoch: 65 -> Loss: 0.0232560932636
Epoch: 65 -> Test Accuracy: 80.17
Epoch: 66 -> Loss: 0.0160289555788
Epoch: 66 -> Test Accuracy: 80.22
Epoch: 67 -> Loss: 0.0497677177191
Epoch: 67 -> Test Accuracy: 80.26
Epoch: 68 -> Loss: 0.0101454406977
Epoch: 68 -> Test Accuracy: 80.25
Epoch: 69 -> Loss: 0.0333657115698
Epoch: 69 -> Test Accuracy: 80.2
Epoch: 70 -> Loss: 0.0100089907646
Epoch: 70 -> Test Accuracy: 80.3
Epoch: 71 -> Loss: 0.0527093932033
Epoch: 71 -> Test Accuracy: 80.39
Epoch: 72 -> Loss: 0.0253549814224
Epoch: 72 -> Test Accuracy: 80.43
Epoch: 73 -> Loss: 0.0171993970871
Epoch: 73 -> Test Accuracy: 80.31
Epoch: 74 -> Loss: 0.0125417411327
Epoch: 74 -> Test Accuracy: 80.37
Epoch: 75 -> Loss: 0.0132678896189
Epoch: 75 -> Test Accuracy: 80.34
Epoch: 76 -> Loss: 0.0169373005629
Epoch: 76 -> Test Accuracy: 80.35
Epoch: 77 -> Loss: 0.0184165239334
Epoch: 77 -> Test Accuracy: 80.35
Epoch: 78 -> Loss: 0.0186169743538
Epoch: 78 -> Test Accuracy: 80.32
Epoch: 79 -> Loss: 0.0897953137755
Epoch: 79 -> Test Accuracy: 80.24
Epoch: 80 -> Loss: 0.0688715130091
Epoch: 80 -> Test Accuracy: 80.2
Epoch: 81 -> Loss: 0.0400617569685
Epoch: 81 -> Test Accuracy: 80.26
Epoch: 82 -> Loss: 0.0169174075127
Epoch: 82 -> Test Accuracy: 80.34
Epoch: 83 -> Loss: 0.0112945735455
Epoch: 83 -> Test Accuracy: 80.33
Epoch: 84 -> Loss: 0.0744367539883
Epoch: 84 -> Test Accuracy: 80.25
Epoch: 85 -> Loss: 0.0124695748091
Epoch: 85 -> Test Accuracy: 80.18
Epoch: 86 -> Loss: 0.00609792768955
Epoch: 86 -> Test Accuracy: 80.2
Epoch: 87 -> Loss: 0.0456972122192
Epoch: 87 -> Test Accuracy: 80.22
Epoch: 88 -> Loss: 0.0483660250902
Epoch: 88 -> Test Accuracy: 80.17
Epoch: 89 -> Loss: 0.0139588862658
Epoch: 89 -> Test Accuracy: 80.19
Epoch: 90 -> Loss: 0.0116586536169
Epoch: 90 -> Test Accuracy: 80.21
Epoch: 91 -> Loss: 0.00879560410976
Epoch: 91 -> Test Accuracy: 80.21
Epoch: 92 -> Loss: 0.0147249400616
Epoch: 92 -> Test Accuracy: 80.2
Epoch: 93 -> Loss: 0.0143815279007
Epoch: 93 -> Test Accuracy: 80.2
Epoch: 94 -> Loss: 0.00966900587082
Epoch: 94 -> Test Accuracy: 80.18
Epoch: 95 -> Loss: 0.0102178305387
Epoch: 95 -> Test Accuracy: 80.2
Epoch: 96 -> Loss: 0.0182191133499
Epoch: 96 -> Test Accuracy: 80.22
Epoch: 97 -> Loss: 0.073236182332
Epoch: 97 -> Test Accuracy: 80.21
Epoch: 98 -> Loss: 0.0175753235817
Epoch: 98 -> Test Accuracy: 80.21
Epoch: 99 -> Loss: 0.0189524143934
Epoch: 99 -> Test Accuracy: 80.2
Epoch: 100 -> Loss: 0.00667914748192
Epoch: 100 -> Test Accuracy: 80.19
Finished Training
Epoch: 1 -> Loss: 1.8052713871
Epoch: 1 -> Test Accuracy: 32.78
Epoch: 2 -> Loss: 1.4549536705
Epoch: 2 -> Test Accuracy: 38.77
Epoch: 3 -> Loss: 1.74625742435
Epoch: 3 -> Test Accuracy: 42.13
Epoch: 4 -> Loss: 1.45812571049
Epoch: 4 -> Test Accuracy: 44.11
Epoch: 5 -> Loss: 1.23404753208
Epoch: 5 -> Test Accuracy: 46.83
Epoch: 6 -> Loss: 1.29505217075
Epoch: 6 -> Test Accuracy: 49.02
Epoch: 7 -> Loss: 0.808918893337
Epoch: 7 -> Test Accuracy: 44.8
Epoch: 8 -> Loss: 1.09893918037
Epoch: 8 -> Test Accuracy: 52.3
Epoch: 9 -> Loss: 1.08894097805
Epoch: 9 -> Test Accuracy: 53.42
Epoch: 10 -> Loss: 0.857489943504
Epoch: 10 -> Test Accuracy: 49.95
Epoch: 11 -> Loss: 1.08701896667
Epoch: 11 -> Test Accuracy: 54.2
Epoch: 12 -> Loss: 0.827267110348
Epoch: 12 -> Test Accuracy: 57.24
Epoch: 13 -> Loss: 1.14055275917
Epoch: 13 -> Test Accuracy: 55.29
Epoch: 14 -> Loss: 1.33272373676
Epoch: 14 -> Test Accuracy: 56.12
Epoch: 15 -> Loss: 0.502484440804
Epoch: 15 -> Test Accuracy: 56.8
Epoch: 16 -> Loss: 0.669791579247
Epoch: 16 -> Test Accuracy: 61.3
Epoch: 17 -> Loss: 0.920004606247
Epoch: 17 -> Test Accuracy: 56.2
Epoch: 18 -> Loss: 1.01290249825
Epoch: 18 -> Test Accuracy: 58.91
Epoch: 19 -> Loss: 0.661690473557
Epoch: 19 -> Test Accuracy: 60.98
Epoch: 20 -> Loss: 0.868436694145
Epoch: 20 -> Test Accuracy: 60.56
Epoch: 21 -> Loss: 0.431213438511
Epoch: 21 -> Test Accuracy: 61.11
Epoch: 22 -> Loss: 0.77817940712
Epoch: 22 -> Test Accuracy: 56.13
Epoch: 23 -> Loss: 0.620285212994
Epoch: 23 -> Test Accuracy: 55.99
Epoch: 24 -> Loss: 0.864762067795
Epoch: 24 -> Test Accuracy: 62.75
Epoch: 25 -> Loss: 1.06970930099
Epoch: 25 -> Test Accuracy: 59.68
Epoch: 26 -> Loss: 0.549698770046
Epoch: 26 -> Test Accuracy: 62.42
Epoch: 27 -> Loss: 0.580783247948
Epoch: 27 -> Test Accuracy: 64.4
Epoch: 28 -> Loss: 0.757976233959
Epoch: 28 -> Test Accuracy: 61.52
Epoch: 29 -> Loss: 0.73870486021
Epoch: 29 -> Test Accuracy: 61.59
Epoch: 30 -> Loss: 0.508637487888
Epoch: 30 -> Test Accuracy: 63.01
Epoch: 31 -> Loss: 0.331197977066
Epoch: 31 -> Test Accuracy: 62.15
Epoch: 32 -> Loss: 0.470873266459
Epoch: 32 -> Test Accuracy: 62.75
Epoch: 33 -> Loss: 0.374791145325
Epoch: 33 -> Test Accuracy: 64.56
Epoch: 34 -> Loss: 0.492821633816
Epoch: 34 -> Test Accuracy: 64.67
Epoch: 35 -> Loss: 0.345730870962
Epoch: 35 -> Test Accuracy: 64.3
Epoch: 36 -> Loss: 0.433670759201
Epoch: 36 -> Test Accuracy: 62.59
Epoch: 37 -> Loss: 0.317645341158
Epoch: 37 -> Test Accuracy: 61.45
Epoch: 38 -> Loss: 0.40732049942
Epoch: 38 -> Test Accuracy: 64.81
Epoch: 39 -> Loss: 0.366106927395
Epoch: 39 -> Test Accuracy: 64.42
Epoch: 40 -> Loss: 0.604485869408
Epoch: 40 -> Test Accuracy: 64.34
Epoch: 41 -> Loss: 0.489239275455
Epoch: 41 -> Test Accuracy: 61.53
Epoch: 42 -> Loss: 0.755823552608
Epoch: 42 -> Test Accuracy: 64.48
Epoch: 43 -> Loss: 0.270692288876
Epoch: 43 -> Test Accuracy: 66.21
Epoch: 44 -> Loss: 0.398351848125
Epoch: 44 -> Test Accuracy: 64.22
Epoch: 45 -> Loss: 0.491998851299
Epoch: 45 -> Test Accuracy: 63.57
Epoch: 46 -> Loss: 0.334801137447
Epoch: 46 -> Test Accuracy: 65.63
Epoch: 47 -> Loss: 0.571182549
Epoch: 47 -> Test Accuracy: 64.95
Epoch: 48 -> Loss: 0.336699008942
Epoch: 48 -> Test Accuracy: 64.46
Epoch: 49 -> Loss: 0.358170062304
Epoch: 49 -> Test Accuracy: 65.28
Epoch: 50 -> Loss: 0.250126063824
Epoch: 50 -> Test Accuracy: 65.64
Epoch: 51 -> Loss: 0.235494241118
Epoch: 51 -> Test Accuracy: 65.97
Epoch: 52 -> Loss: 0.175247132778
Epoch: 52 -> Test Accuracy: 66.37
Epoch: 53 -> Loss: 0.2731808424
Epoch: 53 -> Test Accuracy: 64.96
Epoch: 54 -> Loss: 0.316665738821
Epoch: 54 -> Test Accuracy: 62.65
Epoch: 55 -> Loss: 0.199551001191
Epoch: 55 -> Test Accuracy: 65.93
Epoch: 56 -> Loss: 0.148887112737
Epoch: 56 -> Test Accuracy: 66.13
Epoch: 57 -> Loss: 0.198421061039
Epoch: 57 -> Test Accuracy: 66.73
Epoch: 58 -> Loss: 0.449224352837
Epoch: 58 -> Test Accuracy: 66.5
Epoch: 59 -> Loss: 0.29995071888
Epoch: 59 -> Test Accuracy: 63.8
Epoch: 60 -> Loss: 0.257976233959
Epoch: 60 -> Test Accuracy: 67.68
Epoch: 61 -> Loss: 0.125610470772
Epoch: 61 -> Test Accuracy: 70.04
Epoch: 62 -> Loss: 0.0315936803818
Epoch: 62 -> Test Accuracy: 70.27
Epoch: 63 -> Loss: 0.202752485871
Epoch: 63 -> Test Accuracy: 70.31
Epoch: 64 -> Loss: 0.0247804373503
Epoch: 64 -> Test Accuracy: 70.41
Epoch: 65 -> Loss: 0.0378330647945
Epoch: 65 -> Test Accuracy: 70.53
Epoch: 66 -> Loss: 0.10579136014
Epoch: 66 -> Test Accuracy: 70.43
Epoch: 67 -> Loss: 0.0575700551271
Epoch: 67 -> Test Accuracy: 70.64
Epoch: 68 -> Loss: 0.0453321263194
Epoch: 68 -> Test Accuracy: 70.43
Epoch: 69 -> Loss: 0.0445282012224
Epoch: 69 -> Test Accuracy: 70.64
Epoch: 70 -> Loss: 0.0336372405291
Epoch: 70 -> Test Accuracy: 70.65
Epoch: 71 -> Loss: 0.0675330460072
Epoch: 71 -> Test Accuracy: 70.93
Epoch: 72 -> Loss: 0.0812125056982
Epoch: 72 -> Test Accuracy: 70.8
Epoch: 73 -> Loss: 0.0531407520175
Epoch: 73 -> Test Accuracy: 70.54
Epoch: 74 -> Loss: 0.0309773236513
Epoch: 74 -> Test Accuracy: 71.07
Epoch: 75 -> Loss: 0.0236945524812
Epoch: 75 -> Test Accuracy: 70.87
Epoch: 76 -> Loss: 0.0100391060114
Epoch: 76 -> Test Accuracy: 70.96
Epoch: 77 -> Loss: 0.0159396380186
Epoch: 77 -> Test Accuracy: 71.08
Epoch: 78 -> Loss: 0.0112279355526
Epoch: 78 -> Test Accuracy: 70.71
Epoch: 79 -> Loss: 0.0287714749575
Epoch: 79 -> Test Accuracy: 70.96
Epoch: 80 -> Loss: 0.176479279995
Epoch: 80 -> Test Accuracy: 71.1
Epoch: 81 -> Loss: 0.0553053840995
Epoch: 81 -> Test Accuracy: 70.19
Epoch: 82 -> Loss: 0.0222607702017
Epoch: 82 -> Test Accuracy: 71.05
Epoch: 83 -> Loss: 0.017648845911
Epoch: 83 -> Test Accuracy: 71.11
Epoch: 84 -> Loss: 0.00824658572674
Epoch: 84 -> Test Accuracy: 70.92
Epoch: 85 -> Loss: 0.0197389870882
Epoch: 85 -> Test Accuracy: 71.15
Epoch: 86 -> Loss: 0.0256101638079
Epoch: 86 -> Test Accuracy: 71.0
Epoch: 87 -> Loss: 0.0209289640188
Epoch: 87 -> Test Accuracy: 70.99
Epoch: 88 -> Loss: 0.0267375558615
Epoch: 88 -> Test Accuracy: 71.22
Epoch: 89 -> Loss: 0.0153915286064
Epoch: 89 -> Test Accuracy: 70.98
Epoch: 90 -> Loss: 0.0126494318247
Epoch: 90 -> Test Accuracy: 70.9
Epoch: 91 -> Loss: 0.0112864971161
Epoch: 91 -> Test Accuracy: 70.83
Epoch: 92 -> Loss: 0.00292167067528
Epoch: 92 -> Test Accuracy: 71.16
Epoch: 93 -> Loss: 0.017883643508
Epoch: 93 -> Test Accuracy: 71.19
Epoch: 94 -> Loss: 0.0337622314692
Epoch: 94 -> Test Accuracy: 71.23
Epoch: 95 -> Loss: 0.0120377838612
Epoch: 95 -> Test Accuracy: 71.1
Epoch: 96 -> Loss: 0.0127462744713
Epoch: 96 -> Test Accuracy: 71.11
Epoch: 97 -> Loss: 0.0171550512314
Epoch: 97 -> Test Accuracy: 71.11
Epoch: 98 -> Loss: 0.0599996745586
Epoch: 98 -> Test Accuracy: 70.76
Epoch: 99 -> Loss: 0.00941671431065
Epoch: 99 -> Test Accuracy: 70.31
Epoch: 100 -> Loss: 0.0182780325413
Epoch: 100 -> Test Accuracy: 70.87
Epoch: 101 -> Loss: 0.00950425863266
Epoch: 101 -> Test Accuracy: 70.9
Epoch: 102 -> Loss: 0.00967802107334
Epoch: 102 -> Test Accuracy: 70.83
Epoch: 103 -> Loss: 0.00724190473557
Epoch: 103 -> Test Accuracy: 70.64
Epoch: 104 -> Loss: 0.0324417054653
Epoch: 104 -> Test Accuracy: 70.91
Epoch: 105 -> Loss: 0.0169687718153
Epoch: 105 -> Test Accuracy: 71.13
Epoch: 106 -> Loss: 0.0314528793097
Epoch: 106 -> Test Accuracy: 71.1
Epoch: 107 -> Loss: 0.0214038491249
Epoch: 107 -> Test Accuracy: 70.72
Epoch: 108 -> Loss: 0.0333647802472
Epoch: 108 -> Test Accuracy: 70.83
Epoch: 109 -> Loss: 0.020948767662
Epoch: 109 -> Test Accuracy: 70.48
Epoch: 110 -> Loss: 0.0321883633733
Epoch: 110 -> Test Accuracy: 70.69
Epoch: 111 -> Loss: 0.0491126775742
Epoch: 111 -> Test Accuracy: 70.94
Epoch: 112 -> Loss: 0.0477923154831
Epoch: 112 -> Test Accuracy: 70.67
Epoch: 113 -> Loss: 0.0112421363592
Epoch: 113 -> Test Accuracy: 70.71
Epoch: 114 -> Loss: 0.0729033946991
Epoch: 114 -> Test Accuracy: 70.82
Epoch: 115 -> Loss: 0.0184053480625
Epoch: 115 -> Test Accuracy: 70.35
Epoch: 116 -> Loss: 0.0135750174522
Epoch: 116 -> Test Accuracy: 70.74
Epoch: 117 -> Loss: 0.0123312175274
Epoch: 117 -> Test Accuracy: 70.85
Epoch: 118 -> Loss: 0.0110506862402
Epoch: 118 -> Test Accuracy: 70.93
Epoch: 119 -> Loss: 0.0266265869141
Epoch: 119 -> Test Accuracy: 70.97
Epoch: 120 -> Loss: 0.00591425597668
Epoch: 120 -> Test Accuracy: 71.12
Epoch: 121 -> Loss: 0.0119008272886
Epoch: 121 -> Test Accuracy: 71.06
Epoch: 122 -> Loss: 0.0214089006186
Epoch: 122 -> Test Accuracy: 71.1
Epoch: 123 -> Loss: 0.050809442997
Epoch: 123 -> Test Accuracy: 70.96
Epoch: 124 -> Loss: 0.00507043302059
Epoch: 124 -> Test Accuracy: 70.89
Epoch: 125 -> Loss: 0.0147071629763
Epoch: 125 -> Test Accuracy: 70.99
Epoch: 126 -> Loss: 0.0269448906183
Epoch: 126 -> Test Accuracy: 71.06
Epoch: 127 -> Loss: 0.0107784122229
Epoch: 127 -> Test Accuracy: 71.08
Epoch: 128 -> Loss: 0.00431968271732
Epoch: 128 -> Test Accuracy: 71.14
Epoch: 129 -> Loss: 0.0145100802183
Epoch: 129 -> Test Accuracy: 71.1
Epoch: 130 -> Loss: 0.0196961015463
Epoch: 130 -> Test Accuracy: 71.08
Epoch: 131 -> Loss: 0.00568276643753
Epoch: 131 -> Test Accuracy: 71.23
Epoch: 132 -> Loss: 0.00373020768166
Epoch: 132 -> Test Accuracy: 71.15
Epoch: 133 -> Loss: 0.0101871192455
Epoch: 133 -> Test Accuracy: 71.14
Epoch: 134 -> Loss: 0.00699551403522
Epoch: 134 -> Test Accuracy: 71.19
Epoch: 135 -> Loss: 0.00743374228477
Epoch: 135 -> Test Accuracy: 71.2
Epoch: 136 -> Loss: 0.00587114691734
Epoch: 136 -> Test Accuracy: 71.08
Epoch: 137 -> Loss: 0.0149262398481
Epoch: 137 -> Test Accuracy: 71.32
Epoch: 138 -> Loss: 0.0398357957602
Epoch: 138 -> Test Accuracy: 71.19
Epoch: 139 -> Loss: 0.0252690389752
Epoch: 139 -> Test Accuracy: 71.1
Epoch: 140 -> Loss: 0.00424282252789
Epoch: 140 -> Test Accuracy: 71.22
Epoch: 141 -> Loss: 0.0226623117924
Epoch: 141 -> Test Accuracy: 71.23
Epoch: 142 -> Loss: 0.00834879279137
Epoch: 142 -> Test Accuracy: 71.3
Epoch: 143 -> Loss: 0.0448399782181
Epoch: 143 -> Test Accuracy: 71.22
Epoch: 144 -> Loss: 0.011642575264
Epoch: 144 -> Test Accuracy: 71.32
Epoch: 145 -> Loss: 0.00537167489529
Epoch: 145 -> Test Accuracy: 71.22
Epoch: 146 -> Loss: 0.0025320649147
Epoch: 146 -> Test Accuracy: 71.17
Epoch: 147 -> Loss: 0.0120529234409
Epoch: 147 -> Test Accuracy: 71.12
Epoch: 148 -> Loss: 0.0126224458218
Epoch: 148 -> Test Accuracy: 71.07
Epoch: 149 -> Loss: 0.00870415568352
Epoch: 149 -> Test Accuracy: 71.23
Epoch: 150 -> Loss: 0.00486120581627
Epoch: 150 -> Test Accuracy: 71.23
Epoch: 151 -> Loss: 0.0206541866064
Epoch: 151 -> Test Accuracy: 71.16
Epoch: 152 -> Loss: 0.0155073255301
Epoch: 152 -> Test Accuracy: 71.34
Epoch: 153 -> Loss: 0.00852030515671
Epoch: 153 -> Test Accuracy: 71.19
Epoch: 154 -> Loss: 0.0849987491965
Epoch: 154 -> Test Accuracy: 71.18
Epoch: 155 -> Loss: 0.0158245265484
Epoch: 155 -> Test Accuracy: 71.21
Epoch: 156 -> Loss: 0.0144473612309
Epoch: 156 -> Test Accuracy: 71.18
Epoch: 157 -> Loss: 0.0236957073212
Epoch: 157 -> Test Accuracy: 71.09
Epoch: 158 -> Loss: 0.00618058443069
Epoch: 158 -> Test Accuracy: 71.1
Epoch: 159 -> Loss: 0.00511218607426
Epoch: 159 -> Test Accuracy: 71.08
Epoch: 160 -> Loss: 0.00768600404263
Epoch: 160 -> Test Accuracy: 71.06
Epoch: 161 -> Loss: 0.0148626118898
Epoch: 161 -> Test Accuracy: 71.07
Epoch: 162 -> Loss: 0.00924523174763
Epoch: 162 -> Test Accuracy: 71.04
Epoch: 163 -> Loss: 0.0148717164993
Epoch: 163 -> Test Accuracy: 71.06
Epoch: 164 -> Loss: 0.0135444998741
Epoch: 164 -> Test Accuracy: 71.03
Epoch: 165 -> Loss: 0.00910976529121
Epoch: 165 -> Test Accuracy: 71.02
Epoch: 166 -> Loss: 0.0085741430521
Epoch: 166 -> Test Accuracy: 70.95
Epoch: 167 -> Loss: 0.00706927478313
Epoch: 167 -> Test Accuracy: 70.96
Epoch: 168 -> Loss: 0.00602598488331
Epoch: 168 -> Test Accuracy: 70.98
Epoch: 169 -> Loss: 0.0110482275486
Epoch: 169 -> Test Accuracy: 71.06
Epoch: 170 -> Loss: 0.0153047293425
Epoch: 170 -> Test Accuracy: 71.04
Epoch: 171 -> Loss: 0.0675692558289
Epoch: 171 -> Test Accuracy: 71.07
Epoch: 172 -> Loss: 0.039071649313
Epoch: 172 -> Test Accuracy: 71.06
Epoch: 173 -> Loss: 0.00860622525215
Epoch: 173 -> Test Accuracy: 71.03
Epoch: 174 -> Loss: 0.0049963593483
Epoch: 174 -> Test Accuracy: 71.03
Epoch: 175 -> Loss: 0.0280245244503
Epoch: 175 -> Test Accuracy: 71.03
Epoch: 176 -> Loss: 0.00963750481606
Epoch: 176 -> Test Accuracy: 71.03
Epoch: 177 -> Loss: 0.0141156464815
Epoch: 177 -> Test Accuracy: 71.04
Epoch: 178 -> Loss: 0.0196160078049
Epoch: 178 -> Test Accuracy: 71.13
Epoch: 179 -> Loss: 0.00304661691189
Epoch: 179 -> Test Accuracy: 71.12
Epoch: 180 -> Loss: 0.00694055855274
Epoch: 180 -> Test Accuracy: 71.1
Epoch: 181 -> Loss: 0.00450487434864
Epoch: 181 -> Test Accuracy: 71.15
Epoch: 182 -> Loss: 0.00474372506142
Epoch: 182 -> Test Accuracy: 71.2
Epoch: 183 -> Loss: 0.00594429671764
Epoch: 183 -> Test Accuracy: 71.14
Epoch: 184 -> Loss: 0.0180179476738
Epoch: 184 -> Test Accuracy: 71.1
Epoch: 185 -> Loss: 0.00465805828571
Epoch: 185 -> Test Accuracy: 71.11
Epoch: 186 -> Loss: 0.00408010184765
Epoch: 186 -> Test Accuracy: 71.14
Epoch: 187 -> Loss: 0.0138216912746
Epoch: 187 -> Test Accuracy: 71.11
Epoch: 188 -> Loss: 0.0191442668438
Epoch: 188 -> Test Accuracy: 71.12
Epoch: 189 -> Loss: 0.0226793438196
Epoch: 189 -> Test Accuracy: 71.1
Epoch: 190 -> Loss: 0.00964859127998
Epoch: 190 -> Test Accuracy: 71.12
Epoch: 191 -> Loss: 0.0184281468391
Epoch: 191 -> Test Accuracy: 71.12
Epoch: 192 -> Loss: 0.0143183246255
Epoch: 192 -> Test Accuracy: 71.08
Epoch: 193 -> Loss: 0.0082062035799
Epoch: 193 -> Test Accuracy: 71.11
Epoch: 194 -> Loss: 0.00283917784691
Epoch: 194 -> Test Accuracy: 71.13
Epoch: 195 -> Loss: 0.0825093537569
Epoch: 195 -> Test Accuracy: 71.03
Epoch: 196 -> Loss: 0.02463606745
Epoch: 196 -> Test Accuracy: 71.02
Epoch: 197 -> Loss: 0.0977230519056
Epoch: 197 -> Test Accuracy: 71.04
Epoch: 198 -> Loss: 0.019749417901
Epoch: 198 -> Test Accuracy: 71.13
Epoch: 199 -> Loss: 0.0182576626539
Epoch: 199 -> Test Accuracy: 71.19
Epoch: 200 -> Loss: 0.0168407261372
Epoch: 200 -> Test Accuracy: 71.12
Finished Training
[1, 60] loss: 0.960
Epoch: 1 -> Loss: 1.38447475433
Epoch: 1 -> Test Accuracy: 73.28
[2, 60] loss: 0.571
Epoch: 2 -> Loss: 0.404527902603
Epoch: 2 -> Test Accuracy: 75.4
[3, 60] loss: 0.479
Epoch: 3 -> Loss: 0.560607910156
Epoch: 3 -> Test Accuracy: 78.34
[4, 60] loss: 0.426
Epoch: 4 -> Loss: 0.151405870914
Epoch: 4 -> Test Accuracy: 77.57
[5, 60] loss: 0.379
Epoch: 5 -> Loss: 0.439147114754
Epoch: 5 -> Test Accuracy: 79.42
[6, 60] loss: 0.350
Epoch: 6 -> Loss: 0.310629814863
Epoch: 6 -> Test Accuracy: 79.34
[7, 60] loss: 0.315
Epoch: 7 -> Loss: 0.591866612434
Epoch: 7 -> Test Accuracy: 79.06
[8, 60] loss: 0.304
Epoch: 8 -> Loss: 0.391358375549
Epoch: 8 -> Test Accuracy: 80.29
[9, 60] loss: 0.267
Epoch: 9 -> Loss: 0.220833972096
Epoch: 9 -> Test Accuracy: 80.21
[10, 60] loss: 0.255
Epoch: 10 -> Loss: 0.748624145985
Epoch: 10 -> Test Accuracy: 80.01
[11, 60] loss: 0.248
Epoch: 11 -> Loss: 0.29417347908
Epoch: 11 -> Test Accuracy: 77.72
[12, 60] loss: 0.229
Epoch: 12 -> Loss: 0.310448586941
Epoch: 12 -> Test Accuracy: 78.06
[13, 60] loss: 0.219
Epoch: 13 -> Loss: 0.148708105087
Epoch: 13 -> Test Accuracy: 80.37
[14, 60] loss: 0.181
Epoch: 14 -> Loss: 0.339802145958
Epoch: 14 -> Test Accuracy: 79.23
[15, 60] loss: 0.207
Epoch: 15 -> Loss: 0.260935544968
Epoch: 15 -> Test Accuracy: 81.01
[16, 60] loss: 0.191
Epoch: 16 -> Loss: 1.43244659901
Epoch: 16 -> Test Accuracy: 79.17
[17, 60] loss: 0.216
Epoch: 17 -> Loss: 0.360293626785
Epoch: 17 -> Test Accuracy: 80.25
[18, 60] loss: 0.168
Epoch: 18 -> Loss: 0.362103998661
Epoch: 18 -> Test Accuracy: 80.81
[19, 60] loss: 0.151
Epoch: 19 -> Loss: 0.0561759769917
Epoch: 19 -> Test Accuracy: 81.12
[20, 60] loss: 0.134
Epoch: 20 -> Loss: 0.0935906171799
Epoch: 20 -> Test Accuracy: 81.5
[21, 60] loss: 0.126
Epoch: 21 -> Loss: 0.171662181616
Epoch: 21 -> Test Accuracy: 81.99
[22, 60] loss: 0.133
Epoch: 22 -> Loss: 0.497239291668
Epoch: 22 -> Test Accuracy: 78.97
[23, 60] loss: 0.169
Epoch: 23 -> Loss: 0.62078088522
Epoch: 23 -> Test Accuracy: 79.45
[24, 60] loss: 0.179
Epoch: 24 -> Loss: 0.186175227165
Epoch: 24 -> Test Accuracy: 80.74
[25, 60] loss: 0.140
Epoch: 25 -> Loss: 0.055052369833
Epoch: 25 -> Test Accuracy: 80.33
[26, 60] loss: 0.111
Epoch: 26 -> Loss: 0.30346468091
Epoch: 26 -> Test Accuracy: 79.85
[27, 60] loss: 0.145
Epoch: 27 -> Loss: 0.2309871912
Epoch: 27 -> Test Accuracy: 78.44
[28, 60] loss: 0.142
Epoch: 28 -> Loss: 0.420533716679
Epoch: 28 -> Test Accuracy: 79.2
[29, 60] loss: 0.132
Epoch: 29 -> Loss: 0.0760518461466
Epoch: 29 -> Test Accuracy: 81.17
[30, 60] loss: 0.101
Epoch: 30 -> Loss: 0.219887733459
Epoch: 30 -> Test Accuracy: 79.51
[31, 60] loss: 0.114
Epoch: 31 -> Loss: 0.158827662468
Epoch: 31 -> Test Accuracy: 81.09
[32, 60] loss: 0.106
Epoch: 32 -> Loss: 0.0881378501654
Epoch: 32 -> Test Accuracy: 81.58
[33, 60] loss: 0.102
Epoch: 33 -> Loss: 0.239914447069
Epoch: 33 -> Test Accuracy: 80.56
[34, 60] loss: 0.123
Epoch: 34 -> Loss: 0.0889967381954
Epoch: 34 -> Test Accuracy: 82.02
[35, 60] loss: 0.099
Epoch: 35 -> Loss: 0.120920062065
Epoch: 35 -> Test Accuracy: 81.03
[36, 60] loss: 0.058
Epoch: 36 -> Loss: 0.0401159524918
Epoch: 36 -> Test Accuracy: 82.65
[37, 60] loss: 0.038
Epoch: 37 -> Loss: 0.100469261408
Epoch: 37 -> Test Accuracy: 83.34
[38, 60] loss: 0.033
Epoch: 38 -> Loss: 0.158164441586
Epoch: 38 -> Test Accuracy: 83.5
[39, 60] loss: 0.028
Epoch: 39 -> Loss: 0.0819121599197
Epoch: 39 -> Test Accuracy: 83.29
[40, 60] loss: 0.029
Epoch: 40 -> Loss: 0.0962285101414
Epoch: 40 -> Test Accuracy: 83.27
[41, 60] loss: 0.026
Epoch: 41 -> Loss: 0.148284494877
Epoch: 41 -> Test Accuracy: 83.42
[42, 60] loss: 0.025
Epoch: 42 -> Loss: 0.0750087499619
Epoch: 42 -> Test Accuracy: 83.54
[43, 60] loss: 0.023
Epoch: 43 -> Loss: 0.054880425334
Epoch: 43 -> Test Accuracy: 83.61
[44, 60] loss: 0.020
Epoch: 44 -> Loss: 0.0736004412174
Epoch: 44 -> Test Accuracy: 83.35
[45, 60] loss: 0.022
Epoch: 45 -> Loss: 0.0271249115467
Epoch: 45 -> Test Accuracy: 83.66
[46, 60] loss: 0.018
Epoch: 46 -> Loss: 0.0353977680206
Epoch: 46 -> Test Accuracy: 83.51
[47, 60] loss: 0.019
Epoch: 47 -> Loss: 0.227512463927
Epoch: 47 -> Test Accuracy: 83.55
[48, 60] loss: 0.021
Epoch: 48 -> Loss: 0.0304708480835
Epoch: 48 -> Test Accuracy: 83.54
[49, 60] loss: 0.018
Epoch: 49 -> Loss: 0.408481448889
Epoch: 49 -> Test Accuracy: 83.31
[50, 60] loss: 0.028
Epoch: 50 -> Loss: 0.0528238713741
Epoch: 50 -> Test Accuracy: 82.97
[51, 60] loss: 0.018
Epoch: 51 -> Loss: 0.0792226493359
Epoch: 51 -> Test Accuracy: 83.25
[52, 60] loss: 0.017
Epoch: 52 -> Loss: 0.0469386875629
Epoch: 52 -> Test Accuracy: 83.09
[53, 60] loss: 0.018
Epoch: 53 -> Loss: 0.153931587934
Epoch: 53 -> Test Accuracy: 83.12
[54, 60] loss: 0.023
Epoch: 54 -> Loss: 0.140061914921
Epoch: 54 -> Test Accuracy: 83.23
[55, 60] loss: 0.019
Epoch: 55 -> Loss: 0.193389177322
Epoch: 55 -> Test Accuracy: 83.54
[56, 60] loss: 0.019
Epoch: 56 -> Loss: 0.00955620408058
Epoch: 56 -> Test Accuracy: 83.5
[57, 60] loss: 0.015
Epoch: 57 -> Loss: 0.122540384531
Epoch: 57 -> Test Accuracy: 83.4
[58, 60] loss: 0.019
Epoch: 58 -> Loss: 0.29047998786
Epoch: 58 -> Test Accuracy: 83.24
[59, 60] loss: 0.022
Epoch: 59 -> Loss: 0.027036011219
Epoch: 59 -> Test Accuracy: 83.45
[60, 60] loss: 0.014
Epoch: 60 -> Loss: 0.0282133221626
Epoch: 60 -> Test Accuracy: 83.49
[61, 60] loss: 0.016
Epoch: 61 -> Loss: 0.0138196647167
Epoch: 61 -> Test Accuracy: 83.19
[62, 60] loss: 0.016
Epoch: 62 -> Loss: 0.0315833091736
Epoch: 62 -> Test Accuracy: 83.28
[63, 60] loss: 0.014
Epoch: 63 -> Loss: 0.0696693360806
Epoch: 63 -> Test Accuracy: 83.44
[64, 60] loss: 0.014
Epoch: 64 -> Loss: 0.140820860863
Epoch: 64 -> Test Accuracy: 83.12
[65, 60] loss: 0.017
Epoch: 65 -> Loss: 0.0753938853741
Epoch: 65 -> Test Accuracy: 83.09
[66, 60] loss: 0.017
Epoch: 66 -> Loss: 0.0540786981583
Epoch: 66 -> Test Accuracy: 83.33
[67, 60] loss: 0.015
Epoch: 67 -> Loss: 0.0456347167492
Epoch: 67 -> Test Accuracy: 83.41
[68, 60] loss: 0.014
Epoch: 68 -> Loss: 0.0100956559181
Epoch: 68 -> Test Accuracy: 82.99
[69, 60] loss: 0.012
Epoch: 69 -> Loss: 0.0364240407944
Epoch: 69 -> Test Accuracy: 83.42
[70, 60] loss: 0.013
Epoch: 70 -> Loss: 0.121539384127
Epoch: 70 -> Test Accuracy: 83.31
[71, 60] loss: 0.012
Epoch: 71 -> Loss: 0.175175726414
Epoch: 71 -> Test Accuracy: 83.38
[72, 60] loss: 0.012
Epoch: 72 -> Loss: 0.0455166697502
Epoch: 72 -> Test Accuracy: 83.26
[73, 60] loss: 0.013
Epoch: 73 -> Loss: 0.021762907505
Epoch: 73 -> Test Accuracy: 83.38
[74, 60] loss: 0.011
Epoch: 74 -> Loss: 0.0601827204227
Epoch: 74 -> Test Accuracy: 83.43
[75, 60] loss: 0.011
Epoch: 75 -> Loss: 0.0429280698299
Epoch: 75 -> Test Accuracy: 83.27
[76, 60] loss: 0.011
Epoch: 76 -> Loss: 0.0245901346207
Epoch: 76 -> Test Accuracy: 83.35
[77, 60] loss: 0.011
Epoch: 77 -> Loss: 0.009725689888
Epoch: 77 -> Test Accuracy: 83.41
[78, 60] loss: 0.012
Epoch: 78 -> Loss: 0.13119417429
Epoch: 78 -> Test Accuracy: 83.41
[79, 60] loss: 0.011
Epoch: 79 -> Loss: 0.0184655189514
Epoch: 79 -> Test Accuracy: 83.42
[80, 60] loss: 0.011
Epoch: 80 -> Loss: 0.00688621401787
Epoch: 80 -> Test Accuracy: 83.61
[81, 60] loss: 0.010
Epoch: 81 -> Loss: 0.0176421999931
Epoch: 81 -> Test Accuracy: 83.62
[82, 60] loss: 0.011
Epoch: 82 -> Loss: 0.0340698361397
Epoch: 82 -> Test Accuracy: 83.49
[83, 60] loss: 0.010
Epoch: 83 -> Loss: 0.0454367697239
Epoch: 83 -> Test Accuracy: 83.51
[84, 60] loss: 0.011
Epoch: 84 -> Loss: 0.0988063663244
Epoch: 84 -> Test Accuracy: 83.51
[85, 60] loss: 0.011
Epoch: 85 -> Loss: 0.0291695296764
Epoch: 85 -> Test Accuracy: 83.47
[86, 60] loss: 0.011
Epoch: 86 -> Loss: 0.0761667788029
Epoch: 86 -> Test Accuracy: 83.49
[87, 60] loss: 0.010
Epoch: 87 -> Loss: 0.0103091299534
Epoch: 87 -> Test Accuracy: 83.54
[88, 60] loss: 0.009
Epoch: 88 -> Loss: 0.0679731965065
Epoch: 88 -> Test Accuracy: 83.56
[89, 60] loss: 0.010
Epoch: 89 -> Loss: 0.0757903307676
Epoch: 89 -> Test Accuracy: 83.63
[90, 60] loss: 0.010
Epoch: 90 -> Loss: 0.0155855417252
Epoch: 90 -> Test Accuracy: 83.56
[91, 60] loss: 0.010
Epoch: 91 -> Loss: 0.0333587527275
Epoch: 91 -> Test Accuracy: 83.55
[92, 60] loss: 0.010
Epoch: 92 -> Loss: 0.472617059946
Epoch: 92 -> Test Accuracy: 83.62
[93, 60] loss: 0.011
Epoch: 93 -> Loss: 0.0198042690754
Epoch: 93 -> Test Accuracy: 83.69
[94, 60] loss: 0.010
Epoch: 94 -> Loss: 0.0703645050526
Epoch: 94 -> Test Accuracy: 83.66
[95, 60] loss: 0.010
Epoch: 95 -> Loss: 0.013804256916
Epoch: 95 -> Test Accuracy: 83.61
[96, 60] loss: 0.010
Epoch: 96 -> Loss: 0.191430211067
Epoch: 96 -> Test Accuracy: 83.63
[97, 60] loss: 0.010
Epoch: 97 -> Loss: 0.216685041785
Epoch: 97 -> Test Accuracy: 83.63
[98, 60] loss: 0.009
Epoch: 98 -> Loss: 0.00713688135147
Epoch: 98 -> Test Accuracy: 83.63
[99, 60] loss: 0.010
Epoch: 99 -> Loss: 0.221191763878
Epoch: 99 -> Test Accuracy: 83.63
[100, 60] loss: 0.010
Epoch: 100 -> Loss: 0.132191136479
Epoch: 100 -> Test Accuracy: 83.67
Finished Training
[1, 60] loss: 1.871
Epoch: 1 -> Loss: 1.8187918663
Epoch: 1 -> Test Accuracy: 39.41
[2, 60] loss: 1.493
Epoch: 2 -> Loss: 1.33181905746
Epoch: 2 -> Test Accuracy: 45.18
[3, 60] loss: 1.339
Epoch: 3 -> Loss: 1.09351527691
Epoch: 3 -> Test Accuracy: 51.28
[4, 60] loss: 1.192
Epoch: 4 -> Loss: 1.72012042999
Epoch: 4 -> Test Accuracy: 49.4
[5, 60] loss: 1.110
Epoch: 5 -> Loss: 1.24345839024
Epoch: 5 -> Test Accuracy: 53.96
[6, 60] loss: 1.005
Epoch: 6 -> Loss: 0.790222465992
Epoch: 6 -> Test Accuracy: 54.27
[7, 60] loss: 0.943
Epoch: 7 -> Loss: 1.14126420021
Epoch: 7 -> Test Accuracy: 59.91
[8, 60] loss: 0.888
Epoch: 8 -> Loss: 1.16405045986
Epoch: 8 -> Test Accuracy: 58.87
[9, 60] loss: 0.829
Epoch: 9 -> Loss: 1.30415916443
Epoch: 9 -> Test Accuracy: 64.33
[10, 60] loss: 0.784
Epoch: 10 -> Loss: 0.877873182297
Epoch: 10 -> Test Accuracy: 64.45
[11, 60] loss: 0.733
Epoch: 11 -> Loss: 0.810325264931
Epoch: 11 -> Test Accuracy: 66.8
[12, 60] loss: 0.730
Epoch: 12 -> Loss: 1.31677258015
Epoch: 12 -> Test Accuracy: 68.8
[13, 60] loss: 0.700
Epoch: 13 -> Loss: 1.37249910831
Epoch: 13 -> Test Accuracy: 69.89
[14, 60] loss: 0.667
Epoch: 14 -> Loss: 0.609081327915
Epoch: 14 -> Test Accuracy: 68.89
[15, 60] loss: 0.630
Epoch: 15 -> Loss: 0.806365728378
Epoch: 15 -> Test Accuracy: 68.69
[16, 60] loss: 0.606
Epoch: 16 -> Loss: 0.833701610565
Epoch: 16 -> Test Accuracy: 67.11
[17, 60] loss: 0.587
Epoch: 17 -> Loss: 0.686511933804
Epoch: 17 -> Test Accuracy: 69.92
[18, 60] loss: 0.565
Epoch: 18 -> Loss: 0.609204292297
Epoch: 18 -> Test Accuracy: 67.65
[19, 60] loss: 0.590
Epoch: 19 -> Loss: 0.48525044322
Epoch: 19 -> Test Accuracy: 67.68
[20, 60] loss: 0.555
Epoch: 20 -> Loss: 0.468196600676
Epoch: 20 -> Test Accuracy: 73.53
[21, 60] loss: 0.522
Epoch: 21 -> Loss: 0.628668904305
Epoch: 21 -> Test Accuracy: 72.02
[22, 60] loss: 0.535
Epoch: 22 -> Loss: 1.28545033932
Epoch: 22 -> Test Accuracy: 67.62
[23, 60] loss: 0.537
Epoch: 23 -> Loss: 0.532577753067
Epoch: 23 -> Test Accuracy: 72.39
[24, 60] loss: 0.477
Epoch: 24 -> Loss: 0.578134298325
Epoch: 24 -> Test Accuracy: 72.71
[25, 60] loss: 0.480
Epoch: 25 -> Loss: 0.746399283409
Epoch: 25 -> Test Accuracy: 69.81
[26, 60] loss: 0.499
Epoch: 26 -> Loss: 0.732239842415
Epoch: 26 -> Test Accuracy: 70.0
[27, 60] loss: 0.489
Epoch: 27 -> Loss: 0.29195445776
Epoch: 27 -> Test Accuracy: 72.19
[28, 60] loss: 0.455
Epoch: 28 -> Loss: 0.272972077131
Epoch: 28 -> Test Accuracy: 71.96
[29, 60] loss: 0.412
Epoch: 29 -> Loss: 0.823468923569
Epoch: 29 -> Test Accuracy: 71.78
[30, 60] loss: 0.499
Epoch: 30 -> Loss: 0.551599740982
Epoch: 30 -> Test Accuracy: 71.11
[31, 60] loss: 0.453
Epoch: 31 -> Loss: 0.424916177988
Epoch: 31 -> Test Accuracy: 72.62
[32, 60] loss: 0.421
Epoch: 32 -> Loss: 0.611951828003
Epoch: 32 -> Test Accuracy: 71.06
[33, 60] loss: 0.436
Epoch: 33 -> Loss: 0.61200273037
Epoch: 33 -> Test Accuracy: 72.64
[34, 60] loss: 0.413
Epoch: 34 -> Loss: 0.710877656937
Epoch: 34 -> Test Accuracy: 72.42
[35, 60] loss: 0.407
Epoch: 35 -> Loss: 0.678718626499
Epoch: 35 -> Test Accuracy: 74.43
[36, 60] loss: 0.421
Epoch: 36 -> Loss: 0.393203854561
Epoch: 36 -> Test Accuracy: 69.38
[37, 60] loss: 0.425
Epoch: 37 -> Loss: 0.175001114607
Epoch: 37 -> Test Accuracy: 70.94
[38, 60] loss: 0.370
Epoch: 38 -> Loss: 0.646731853485
Epoch: 38 -> Test Accuracy: 73.25
[39, 60] loss: 0.401
Epoch: 39 -> Loss: 0.202637255192
Epoch: 39 -> Test Accuracy: 73.05
[40, 60] loss: 0.362
Epoch: 40 -> Loss: 0.484558403492
Epoch: 40 -> Test Accuracy: 70.61
[41, 60] loss: 0.359
Epoch: 41 -> Loss: 0.417449980974
Epoch: 41 -> Test Accuracy: 71.93
[42, 60] loss: 0.383
Epoch: 42 -> Loss: 0.453267484903
Epoch: 42 -> Test Accuracy: 73.02
[43, 60] loss: 0.335
Epoch: 43 -> Loss: 0.262421488762
Epoch: 43 -> Test Accuracy: 73.68
[44, 60] loss: 0.334
Epoch: 44 -> Loss: 0.369110047817
Epoch: 44 -> Test Accuracy: 74.82
[45, 60] loss: 0.339
Epoch: 45 -> Loss: 0.433916091919
Epoch: 45 -> Test Accuracy: 71.94
[46, 60] loss: 0.343
Epoch: 46 -> Loss: 0.266774237156
Epoch: 46 -> Test Accuracy: 74.85
[47, 60] loss: 0.332
Epoch: 47 -> Loss: 0.394922554493
Epoch: 47 -> Test Accuracy: 73.22
[48, 60] loss: 0.349
Epoch: 48 -> Loss: 0.722384214401
Epoch: 48 -> Test Accuracy: 73.23
[49, 60] loss: 0.367
Epoch: 49 -> Loss: 0.212504804134
Epoch: 49 -> Test Accuracy: 74.8
[50, 60] loss: 0.301
Epoch: 50 -> Loss: 0.564773797989
Epoch: 50 -> Test Accuracy: 74.47
[51, 60] loss: 0.332
Epoch: 51 -> Loss: 0.477853536606
Epoch: 51 -> Test Accuracy: 69.87
[52, 60] loss: 0.381
Epoch: 52 -> Loss: 0.591364145279
Epoch: 52 -> Test Accuracy: 71.55
[53, 60] loss: 0.333
Epoch: 53 -> Loss: 0.427986323833
Epoch: 53 -> Test Accuracy: 73.85
[54, 60] loss: 0.332
Epoch: 54 -> Loss: 0.29263690114
Epoch: 54 -> Test Accuracy: 74.28
[55, 60] loss: 0.291
Epoch: 55 -> Loss: 0.476581245661
Epoch: 55 -> Test Accuracy: 73.67
[56, 60] loss: 0.323
Epoch: 56 -> Loss: 0.380825281143
Epoch: 56 -> Test Accuracy: 75.12
[57, 60] loss: 0.315
Epoch: 57 -> Loss: 0.319384455681
Epoch: 57 -> Test Accuracy: 75.26
[58, 60] loss: 0.286
Epoch: 58 -> Loss: 0.25169968605
Epoch: 58 -> Test Accuracy: 75.65
[59, 60] loss: 0.302
Epoch: 59 -> Loss: 0.14394120872
Epoch: 59 -> Test Accuracy: 73.31
[60, 60] loss: 0.276
Epoch: 60 -> Loss: 0.502469241619
Epoch: 60 -> Test Accuracy: 72.05
[61, 60] loss: 0.160
Epoch: 61 -> Loss: 0.3072078228
Epoch: 61 -> Test Accuracy: 80.14
[62, 60] loss: 0.093
Epoch: 62 -> Loss: 0.150992333889
Epoch: 62 -> Test Accuracy: 80.71
[63, 60] loss: 0.071
Epoch: 63 -> Loss: 0.07261967659
Epoch: 63 -> Test Accuracy: 80.86
[64, 60] loss: 0.054
Epoch: 64 -> Loss: 0.199264407158
Epoch: 64 -> Test Accuracy: 80.73
[65, 60] loss: 0.058
Epoch: 65 -> Loss: 0.0318068563938
Epoch: 65 -> Test Accuracy: 80.51
[66, 60] loss: 0.046
Epoch: 66 -> Loss: 0.230934843421
Epoch: 66 -> Test Accuracy: 80.54
[67, 60] loss: 0.047
Epoch: 67 -> Loss: 0.0428005158901
Epoch: 67 -> Test Accuracy: 80.49
[68, 60] loss: 0.038
Epoch: 68 -> Loss: 0.119938582182
Epoch: 68 -> Test Accuracy: 80.62
[69, 60] loss: 0.036
Epoch: 69 -> Loss: 0.144359469414
Epoch: 69 -> Test Accuracy: 80.25
[70, 60] loss: 0.032
Epoch: 70 -> Loss: 0.0844859182835
Epoch: 70 -> Test Accuracy: 80.45
[71, 60] loss: 0.033
Epoch: 71 -> Loss: 0.056810721755
Epoch: 71 -> Test Accuracy: 80.65
[72, 60] loss: 0.030
Epoch: 72 -> Loss: 0.112621635199
Epoch: 72 -> Test Accuracy: 80.8
[73, 60] loss: 0.034
Epoch: 73 -> Loss: 0.213060438633
Epoch: 73 -> Test Accuracy: 81.03
[74, 60] loss: 0.033
Epoch: 74 -> Loss: 0.06031447649
Epoch: 74 -> Test Accuracy: 80.91
[75, 60] loss: 0.029
Epoch: 75 -> Loss: 0.000595450401306
Epoch: 75 -> Test Accuracy: 80.92
[76, 60] loss: 0.022
Epoch: 76 -> Loss: 0.193061798811
Epoch: 76 -> Test Accuracy: 81.13
[77, 60] loss: 0.032
Epoch: 77 -> Loss: 0.168416678905
Epoch: 77 -> Test Accuracy: 80.38
[78, 60] loss: 0.035
Epoch: 78 -> Loss: 0.0620669275522
Epoch: 78 -> Test Accuracy: 80.48
[79, 60] loss: 0.025
Epoch: 79 -> Loss: 0.155465066433
Epoch: 79 -> Test Accuracy: 80.85
[80, 60] loss: 0.027
Epoch: 80 -> Loss: 0.0427515655756
Epoch: 80 -> Test Accuracy: 80.84
[81, 60] loss: 0.019
Epoch: 81 -> Loss: 0.0293017029762
Epoch: 81 -> Test Accuracy: 81.0
[82, 60] loss: 0.017
Epoch: 82 -> Loss: 0.0407817363739
Epoch: 82 -> Test Accuracy: 80.85
[83, 60] loss: 0.018
Epoch: 83 -> Loss: 0.065326333046
Epoch: 83 -> Test Accuracy: 80.76
[84, 60] loss: 0.021
Epoch: 84 -> Loss: 0.0887884497643
Epoch: 84 -> Test Accuracy: 80.73
[85, 60] loss: 0.025
Epoch: 85 -> Loss: 0.0692778229713
Epoch: 85 -> Test Accuracy: 80.6
[86, 60] loss: 0.018
Epoch: 86 -> Loss: 0.0024850666523
Epoch: 86 -> Test Accuracy: 80.59
[87, 60] loss: 0.016
Epoch: 87 -> Loss: 0.0340867340565
Epoch: 87 -> Test Accuracy: 80.41
[88, 60] loss: 0.017
Epoch: 88 -> Loss: 0.177219048142
Epoch: 88 -> Test Accuracy: 80.26
[89, 60] loss: 0.026
Epoch: 89 -> Loss: 0.0228471755981
Epoch: 89 -> Test Accuracy: 80.79
[90, 60] loss: 0.014
Epoch: 90 -> Loss: 0.0797606706619
Epoch: 90 -> Test Accuracy: 80.63
[91, 60] loss: 0.019
Epoch: 91 -> Loss: 0.0710729658604
Epoch: 91 -> Test Accuracy: 80.91
[92, 60] loss: 0.021
Epoch: 92 -> Loss: 0.0672790110111
Epoch: 92 -> Test Accuracy: 80.81
[93, 60] loss: 0.019
Epoch: 93 -> Loss: 0.237882465124
Epoch: 93 -> Test Accuracy: 80.25
[94, 60] loss: 0.031
Epoch: 94 -> Loss: 0.0579227209091
Epoch: 94 -> Test Accuracy: 80.37
[95, 60] loss: 0.018
Epoch: 95 -> Loss: 0.158189356327
Epoch: 95 -> Test Accuracy: 80.03
[96, 60] loss: 0.029
Epoch: 96 -> Loss: 0.0466093122959
Epoch: 96 -> Test Accuracy: 80.29
[97, 60] loss: 0.017
Epoch: 97 -> Loss: 0.00992375612259
Epoch: 97 -> Test Accuracy: 80.62
[98, 60] loss: 0.013
Epoch: 98 -> Loss: 0.0287625491619
Epoch: 98 -> Test Accuracy: 80.91
[99, 60] loss: 0.014
Epoch: 99 -> Loss: 0.0344948917627
Epoch: 99 -> Test Accuracy: 80.61
[100, 60] loss: 0.015
Epoch: 100 -> Loss: 0.0827370285988
Epoch: 100 -> Test Accuracy: 80.46
[101, 60] loss: 0.023
Epoch: 101 -> Loss: 0.271556138992
Epoch: 101 -> Test Accuracy: 80.33
[102, 60] loss: 0.041
Epoch: 102 -> Loss: 0.0223966240883
Epoch: 102 -> Test Accuracy: 80.73
[103, 60] loss: 0.019
Epoch: 103 -> Loss: 0.0225029289722
Epoch: 103 -> Test Accuracy: 80.62
[104, 60] loss: 0.013
Epoch: 104 -> Loss: 0.0545526742935
Epoch: 104 -> Test Accuracy: 80.65
[105, 60] loss: 0.016
Epoch: 105 -> Loss: 0.00703835487366
Epoch: 105 -> Test Accuracy: 80.83
[106, 60] loss: 0.011
Epoch: 106 -> Loss: 0.120159447193
Epoch: 106 -> Test Accuracy: 81.06
[107, 60] loss: 0.028
Epoch: 107 -> Loss: 0.0507103055716
Epoch: 107 -> Test Accuracy: 80.4
[108, 60] loss: 0.016
Epoch: 108 -> Loss: 0.00805416703224
Epoch: 108 -> Test Accuracy: 81.16
[109, 60] loss: 0.013
Epoch: 109 -> Loss: 0.0260004997253
Epoch: 109 -> Test Accuracy: 80.19
[110, 60] loss: 0.014
Epoch: 110 -> Loss: 0.0389124155045
Epoch: 110 -> Test Accuracy: 80.75
[111, 60] loss: 0.011
Epoch: 111 -> Loss: 0.0501452684402
Epoch: 111 -> Test Accuracy: 80.69
[112, 60] loss: 0.016
Epoch: 112 -> Loss: 0.110666498542
Epoch: 112 -> Test Accuracy: 80.68
[113, 60] loss: 0.023
Epoch: 113 -> Loss: 0.278691202402
Epoch: 113 -> Test Accuracy: 79.82
[114, 60] loss: 0.042
Epoch: 114 -> Loss: 0.158509463072
Epoch: 114 -> Test Accuracy: 77.98
[115, 60] loss: 0.068
Epoch: 115 -> Loss: 0.116147324443
Epoch: 115 -> Test Accuracy: 80.06
[116, 60] loss: 0.047
Epoch: 116 -> Loss: 0.146273568273
Epoch: 116 -> Test Accuracy: 80.5
[117, 60] loss: 0.039
Epoch: 117 -> Loss: 0.098918274045
Epoch: 117 -> Test Accuracy: 79.59
[118, 60] loss: 0.031
Epoch: 118 -> Loss: 0.199494540691
Epoch: 118 -> Test Accuracy: 80.13
[119, 60] loss: 0.048
Epoch: 119 -> Loss: 0.00287753343582
Epoch: 119 -> Test Accuracy: 80.53
[120, 60] loss: 0.027
Epoch: 120 -> Loss: 0.00842189788818
Epoch: 120 -> Test Accuracy: 80.31
[121, 60] loss: 0.015
Epoch: 121 -> Loss: 0.032810986042
Epoch: 121 -> Test Accuracy: 81.07
[122, 60] loss: 0.012
Epoch: 122 -> Loss: 0.208675101399
Epoch: 122 -> Test Accuracy: 81.25
[123, 60] loss: 0.010
Epoch: 123 -> Loss: 0.0351872444153
Epoch: 123 -> Test Accuracy: 81.3
[124, 60] loss: 0.009
Epoch: 124 -> Loss: 0.0670560002327
Epoch: 124 -> Test Accuracy: 81.48
[125, 60] loss: 0.008
Epoch: 125 -> Loss: 0.0389063954353
Epoch: 125 -> Test Accuracy: 81.43
[126, 60] loss: 0.009
Epoch: 126 -> Loss: 0.0153669714928
Epoch: 126 -> Test Accuracy: 81.69
[127, 60] loss: 0.008
Epoch: 127 -> Loss: 0.281654655933
Epoch: 127 -> Test Accuracy: 81.52
[128, 60] loss: 0.009
Epoch: 128 -> Loss: 0.0480005145073
Epoch: 128 -> Test Accuracy: 81.45
[129, 60] loss: 0.009
Epoch: 129 -> Loss: 0.245687931776
Epoch: 129 -> Test Accuracy: 81.64
[130, 60] loss: 0.010
Epoch: 130 -> Loss: 0.132726043463
Epoch: 130 -> Test Accuracy: 81.44
[131, 60] loss: 0.010
Epoch: 131 -> Loss: 0.0139856338501
Epoch: 131 -> Test Accuracy: 81.35
[132, 60] loss: 0.008
Epoch: 132 -> Loss: 0.111189812422
Epoch: 132 -> Test Accuracy: 81.37
[133, 60] loss: 0.009
Epoch: 133 -> Loss: 0.0187935829163
Epoch: 133 -> Test Accuracy: 81.41
[134, 60] loss: 0.008
Epoch: 134 -> Loss: 0.0202057659626
Epoch: 134 -> Test Accuracy: 81.67
[135, 60] loss: 0.007
Epoch: 135 -> Loss: 0.343096852303
Epoch: 135 -> Test Accuracy: 81.66
[136, 60] loss: 0.013
Epoch: 136 -> Loss: 0.0472999513149
Epoch: 136 -> Test Accuracy: 81.5
[137, 60] loss: 0.008
Epoch: 137 -> Loss: 0.0110380649567
Epoch: 137 -> Test Accuracy: 81.49
[138, 60] loss: 0.007
Epoch: 138 -> Loss: 0.0304985195398
Epoch: 138 -> Test Accuracy: 81.68
[139, 60] loss: 0.007
Epoch: 139 -> Loss: 0.0988726019859
Epoch: 139 -> Test Accuracy: 81.64
[140, 60] loss: 0.008
Epoch: 140 -> Loss: 0.0773797929287
Epoch: 140 -> Test Accuracy: 81.43
[141, 60] loss: 0.008
Epoch: 141 -> Loss: 0.0299672782421
Epoch: 141 -> Test Accuracy: 81.57
[142, 60] loss: 0.007
Epoch: 142 -> Loss: 0.0310593247414
Epoch: 142 -> Test Accuracy: 81.84
[143, 60] loss: 0.006
Epoch: 143 -> Loss: 0.214785695076
Epoch: 143 -> Test Accuracy: 81.57
[144, 60] loss: 0.011
Epoch: 144 -> Loss: 0.024891063571
Epoch: 144 -> Test Accuracy: 81.44
[145, 60] loss: 0.007
Epoch: 145 -> Loss: 0.01173132658
Epoch: 145 -> Test Accuracy: 81.7
[146, 60] loss: 0.006
Epoch: 146 -> Loss: 0.0332079529762
Epoch: 146 -> Test Accuracy: 81.78
[147, 60] loss: 0.005
Epoch: 147 -> Loss: 0.00644749403
Epoch: 147 -> Test Accuracy: 81.74
[148, 60] loss: 0.005
Epoch: 148 -> Loss: 0.00720453262329
Epoch: 148 -> Test Accuracy: 81.76
[149, 60] loss: 0.006
Epoch: 149 -> Loss: 0.135471731424
Epoch: 149 -> Test Accuracy: 81.89
[150, 60] loss: 0.008
Epoch: 150 -> Loss: 0.00206413865089
Epoch: 150 -> Test Accuracy: 81.7
[151, 60] loss: 0.005
Epoch: 151 -> Loss: 0.358989804983
Epoch: 151 -> Test Accuracy: 81.44
[152, 60] loss: 0.012
Epoch: 152 -> Loss: 0.148856312037
Epoch: 152 -> Test Accuracy: 80.7
[153, 60] loss: 0.009
Epoch: 153 -> Loss: 0.00978496670723
Epoch: 153 -> Test Accuracy: 81.46
[154, 60] loss: 0.006
Epoch: 154 -> Loss: 0.027430742979
Epoch: 154 -> Test Accuracy: 81.44
[155, 60] loss: 0.006
Epoch: 155 -> Loss: 0.012020200491
Epoch: 155 -> Test Accuracy: 81.44
[156, 60] loss: 0.006
Epoch: 156 -> Loss: 0.168206393719
Epoch: 156 -> Test Accuracy: 81.46
[157, 60] loss: 0.009
Epoch: 157 -> Loss: 0.106841191649
Epoch: 157 -> Test Accuracy: 81.24
[158, 60] loss: 0.007
Epoch: 158 -> Loss: 0.0978338718414
Epoch: 158 -> Test Accuracy: 81.34
[159, 60] loss: 0.007
Epoch: 159 -> Loss: 0.0140427649021
Epoch: 159 -> Test Accuracy: 81.4
[160, 60] loss: 0.005
Epoch: 160 -> Loss: 0.141021832824
Epoch: 160 -> Test Accuracy: 81.51
[161, 60] loss: 0.006
Epoch: 161 -> Loss: 0.154865205288
Epoch: 161 -> Test Accuracy: 81.46
[162, 60] loss: 0.006
Epoch: 162 -> Loss: 0.016271084547
Epoch: 162 -> Test Accuracy: 81.43
[163, 60] loss: 0.006
Epoch: 163 -> Loss: 0.017687857151
Epoch: 163 -> Test Accuracy: 81.48
[164, 60] loss: 0.006
Epoch: 164 -> Loss: 0.0833943784237
Epoch: 164 -> Test Accuracy: 81.48
[165, 60] loss: 0.006
Epoch: 165 -> Loss: 0.112250342965
Epoch: 165 -> Test Accuracy: 81.5
[166, 60] loss: 0.005
Epoch: 166 -> Loss: 0.350206434727
Epoch: 166 -> Test Accuracy: 81.46
[167, 60] loss: 0.005
Epoch: 167 -> Loss: 0.09053170681
Epoch: 167 -> Test Accuracy: 81.35
[168, 60] loss: 0.006
Epoch: 168 -> Loss: 0.0827461481094
Epoch: 168 -> Test Accuracy: 81.41
[169, 60] loss: 0.006
Epoch: 169 -> Loss: 0.00756841897964
Epoch: 169 -> Test Accuracy: 81.34
[170, 60] loss: 0.005
Epoch: 170 -> Loss: 0.00479540228844
Epoch: 170 -> Test Accuracy: 81.45
[171, 60] loss: 0.005
Epoch: 171 -> Loss: 0.0254593491554
Epoch: 171 -> Test Accuracy: 81.39
[172, 60] loss: 0.005
Epoch: 172 -> Loss: 0.00625348091125
Epoch: 172 -> Test Accuracy: 81.46
[173, 60] loss: 0.005
Epoch: 173 -> Loss: 0.0276162922382
Epoch: 173 -> Test Accuracy: 81.48
[174, 60] loss: 0.005
Epoch: 174 -> Loss: 0.0146744251251
Epoch: 174 -> Test Accuracy: 81.59
[175, 60] loss: 0.005
Epoch: 175 -> Loss: 0.0544751882553
Epoch: 175 -> Test Accuracy: 81.67
[176, 60] loss: 0.005
Epoch: 176 -> Loss: 0.124981999397
Epoch: 176 -> Test Accuracy: 81.62
[177, 60] loss: 0.005
Epoch: 177 -> Loss: 0.00501990318298
Epoch: 177 -> Test Accuracy: 81.56
[178, 60] loss: 0.005
Epoch: 178 -> Loss: 0.227210313082
Epoch: 178 -> Test Accuracy: 81.58
[179, 60] loss: 0.006
Epoch: 179 -> Loss: 0.128590375185
Epoch: 179 -> Test Accuracy: 81.57
[180, 60] loss: 0.005
Epoch: 180 -> Loss: 0.0267832577229
Epoch: 180 -> Test Accuracy: 81.5
[181, 60] loss: 0.005
Epoch: 181 -> Loss: 0.229974016547
Epoch: 181 -> Test Accuracy: 81.63
[182, 60] loss: 0.006
Epoch: 182 -> Loss: 0.00808680057526
Epoch: 182 -> Test Accuracy: 81.52
[183, 60] loss: 0.005
Epoch: 183 -> Loss: 0.183491572738
Epoch: 183 -> Test Accuracy: 81.66
[184, 60] loss: 0.005
Epoch: 184 -> Loss: 0.0509166121483
Epoch: 184 -> Test Accuracy: 81.61
[185, 60] loss: 0.005
Epoch: 185 -> Loss: 0.00560408830643
Epoch: 185 -> Test Accuracy: 81.6
[186, 60] loss: 0.005
Epoch: 186 -> Loss: 0.0282002687454
Epoch: 186 -> Test Accuracy: 81.57
[187, 60] loss: 0.005
Epoch: 187 -> Loss: 0.210005417466
Epoch: 187 -> Test Accuracy: 81.58
[188, 60] loss: 0.005
Epoch: 188 -> Loss: 0.00581669807434
Epoch: 188 -> Test Accuracy: 81.48
[189, 60] loss: 0.005
Epoch: 189 -> Loss: 0.0183884501457
Epoch: 189 -> Test Accuracy: 81.54
[190, 60] loss: 0.005
Epoch: 190 -> Loss: 0.0202942788601
Epoch: 190 -> Test Accuracy: 81.54
[191, 60] loss: 0.005
Epoch: 191 -> Loss: 0.017327696085
Epoch: 191 -> Test Accuracy: 81.6
[192, 60] loss: 0.005
Epoch: 192 -> Loss: 0.0275225937366
Epoch: 192 -> Test Accuracy: 81.5
[193, 60] loss: 0.005
Epoch: 193 -> Loss: 0.00995624065399
Epoch: 193 -> Test Accuracy: 81.47
[194, 60] loss: 0.005
Epoch: 194 -> Loss: 0.0317635238171
Epoch: 194 -> Test Accuracy: 81.57
[195, 60] loss: 0.005
Epoch: 195 -> Loss: 0.0142703950405
Epoch: 195 -> Test Accuracy: 81.55
[196, 60] loss: 0.005
Epoch: 196 -> Loss: 0.0245959460735
Epoch: 196 -> Test Accuracy: 81.52
[197, 60] loss: 0.005
Epoch: 197 -> Loss: 0.0137966871262
Epoch: 197 -> Test Accuracy: 81.5
[198, 60] loss: 0.005
Epoch: 198 -> Loss: 0.0700696408749
Epoch: 198 -> Test Accuracy: 81.45
[199, 60] loss: 0.005
Epoch: 199 -> Loss: 0.10655759275
Epoch: 199 -> Test Accuracy: 81.4
[200, 60] loss: 0.005
Epoch: 200 -> Loss: 0.02673214674
Epoch: 200 -> Test Accuracy: 81.37
Finished Training
[1, 60] loss: 0.923
[1, 120] loss: 0.626
[1, 180] loss: 0.586
[1, 240] loss: 0.536
[1, 300] loss: 0.528
[1, 360] loss: 0.496
Epoch: 1 -> Loss: 0.472029685974
Epoch: 1 -> Test Accuracy: 80.88
[2, 60] loss: 0.439
[2, 120] loss: 0.437
[2, 180] loss: 0.432
[2, 240] loss: 0.454
[2, 300] loss: 0.436
[2, 360] loss: 0.431
Epoch: 2 -> Loss: 0.282150655985
Epoch: 2 -> Test Accuracy: 82.76
[3, 60] loss: 0.375
[3, 120] loss: 0.395
[3, 180] loss: 0.386
[3, 240] loss: 0.411
[3, 300] loss: 0.370
[3, 360] loss: 0.391
Epoch: 3 -> Loss: 0.309844821692
Epoch: 3 -> Test Accuracy: 83.21
[4, 60] loss: 0.351
[4, 120] loss: 0.366
[4, 180] loss: 0.361
[4, 240] loss: 0.362
[4, 300] loss: 0.377
[4, 360] loss: 0.361
Epoch: 4 -> Loss: 0.358151316643
Epoch: 4 -> Test Accuracy: 84.23
[5, 60] loss: 0.331
[5, 120] loss: 0.331
[5, 180] loss: 0.352
[5, 240] loss: 0.343
[5, 300] loss: 0.339
[5, 360] loss: 0.359
Epoch: 5 -> Loss: 0.207544609904
Epoch: 5 -> Test Accuracy: 84.41
[6, 60] loss: 0.313
[6, 120] loss: 0.338
[6, 180] loss: 0.322
[6, 240] loss: 0.331
[6, 300] loss: 0.340
[6, 360] loss: 0.333
Epoch: 6 -> Loss: 0.278534770012
Epoch: 6 -> Test Accuracy: 84.73
[7, 60] loss: 0.295
[7, 120] loss: 0.318
[7, 180] loss: 0.335
[7, 240] loss: 0.317
[7, 300] loss: 0.321
[7, 360] loss: 0.340
Epoch: 7 -> Loss: 0.221092373133
Epoch: 7 -> Test Accuracy: 85.06
[8, 60] loss: 0.289
[8, 120] loss: 0.299
[8, 180] loss: 0.311
[8, 240] loss: 0.316
[8, 300] loss: 0.321
[8, 360] loss: 0.336
Epoch: 8 -> Loss: 0.341584444046
Epoch: 8 -> Test Accuracy: 84.65
[9, 60] loss: 0.282
[9, 120] loss: 0.281
[9, 180] loss: 0.301
[9, 240] loss: 0.342
[9, 300] loss: 0.314
[9, 360] loss: 0.322
Epoch: 9 -> Loss: 0.425087928772
Epoch: 9 -> Test Accuracy: 85.42
[10, 60] loss: 0.277
[10, 120] loss: 0.284
[10, 180] loss: 0.283
[10, 240] loss: 0.295
[10, 300] loss: 0.329
[10, 360] loss: 0.305
Epoch: 10 -> Loss: 0.128976032138
Epoch: 10 -> Test Accuracy: 85.01
[11, 60] loss: 0.275
[11, 120] loss: 0.270
[11, 180] loss: 0.301
[11, 240] loss: 0.298
[11, 300] loss: 0.308
[11, 360] loss: 0.314
Epoch: 11 -> Loss: 0.211427256465
Epoch: 11 -> Test Accuracy: 84.63
[12, 60] loss: 0.279
[12, 120] loss: 0.266
[12, 180] loss: 0.303
[12, 240] loss: 0.291
[12, 300] loss: 0.303
[12, 360] loss: 0.288
Epoch: 12 -> Loss: 0.353486508131
Epoch: 12 -> Test Accuracy: 85.5
[13, 60] loss: 0.264
[13, 120] loss: 0.272
[13, 180] loss: 0.293
[13, 240] loss: 0.308
[13, 300] loss: 0.294
[13, 360] loss: 0.297
Epoch: 13 -> Loss: 0.226709887385
Epoch: 13 -> Test Accuracy: 85.62
[14, 60] loss: 0.263
[14, 120] loss: 0.269
[14, 180] loss: 0.278
[14, 240] loss: 0.290
[14, 300] loss: 0.290
[14, 360] loss: 0.303
Epoch: 14 -> Loss: 0.284619033337
Epoch: 14 -> Test Accuracy: 85.12
[15, 60] loss: 0.273
[15, 120] loss: 0.288
[15, 180] loss: 0.279
[15, 240] loss: 0.285
[15, 300] loss: 0.292
[15, 360] loss: 0.280
Epoch: 15 -> Loss: 0.357054203749
Epoch: 15 -> Test Accuracy: 85.63
[16, 60] loss: 0.256
[16, 120] loss: 0.281
[16, 180] loss: 0.278
[16, 240] loss: 0.279
[16, 300] loss: 0.295
[16, 360] loss: 0.296
Epoch: 16 -> Loss: 0.431780010462
Epoch: 16 -> Test Accuracy: 85.53
[17, 60] loss: 0.257
[17, 120] loss: 0.264
[17, 180] loss: 0.286
[17, 240] loss: 0.285
[17, 300] loss: 0.277
[17, 360] loss: 0.297
Epoch: 17 -> Loss: 0.428378582001
Epoch: 17 -> Test Accuracy: 86.07
[18, 60] loss: 0.259
[18, 120] loss: 0.262
[18, 180] loss: 0.281
[18, 240] loss: 0.272
[18, 300] loss: 0.287
[18, 360] loss: 0.277
Epoch: 18 -> Loss: 0.195079699159
Epoch: 18 -> Test Accuracy: 85.85
[19, 60] loss: 0.256
[19, 120] loss: 0.275
[19, 180] loss: 0.267
[19, 240] loss: 0.279
[19, 300] loss: 0.283
[19, 360] loss: 0.278
Epoch: 19 -> Loss: 0.194859102368
Epoch: 19 -> Test Accuracy: 85.32
[20, 60] loss: 0.238
[20, 120] loss: 0.260
[20, 180] loss: 0.285
[20, 240] loss: 0.281
[20, 300] loss: 0.282
[20, 360] loss: 0.276
Epoch: 20 -> Loss: 0.205233901739
Epoch: 20 -> Test Accuracy: 86.19
[21, 60] loss: 0.238
[21, 120] loss: 0.263
[21, 180] loss: 0.282
[21, 240] loss: 0.288
[21, 300] loss: 0.271
[21, 360] loss: 0.298
Epoch: 21 -> Loss: 0.30584281683
Epoch: 21 -> Test Accuracy: 85.64
[22, 60] loss: 0.258
[22, 120] loss: 0.263
[22, 180] loss: 0.259
[22, 240] loss: 0.269
[22, 300] loss: 0.278
[22, 360] loss: 0.288
Epoch: 22 -> Loss: 0.258524358273
Epoch: 22 -> Test Accuracy: 85.85
[23, 60] loss: 0.254
[23, 120] loss: 0.254
[23, 180] loss: 0.276
[23, 240] loss: 0.284
[23, 300] loss: 0.281
[23, 360] loss: 0.268
Epoch: 23 -> Loss: 0.32976347208
Epoch: 23 -> Test Accuracy: 85.31
[24, 60] loss: 0.257
[24, 120] loss: 0.244
[24, 180] loss: 0.278
[24, 240] loss: 0.276
[24, 300] loss: 0.272
[24, 360] loss: 0.266
Epoch: 24 -> Loss: 0.312492758036
Epoch: 24 -> Test Accuracy: 86.34
[25, 60] loss: 0.254
[25, 120] loss: 0.250
[25, 180] loss: 0.257
[25, 240] loss: 0.266
[25, 300] loss: 0.283
[25, 360] loss: 0.285
Epoch: 25 -> Loss: 0.302136838436
Epoch: 25 -> Test Accuracy: 86.08
[26, 60] loss: 0.248
[26, 120] loss: 0.250
[26, 180] loss: 0.259
[26, 240] loss: 0.258
[26, 300] loss: 0.290
[26, 360] loss: 0.270
Epoch: 26 -> Loss: 0.273910939693
Epoch: 26 -> Test Accuracy: 85.72
[27, 60] loss: 0.234
[27, 120] loss: 0.248
[27, 180] loss: 0.265
[27, 240] loss: 0.278
[27, 300] loss: 0.265
[27, 360] loss: 0.287
Epoch: 27 -> Loss: 0.265882998705
Epoch: 27 -> Test Accuracy: 86.02
[28, 60] loss: 0.236
[28, 120] loss: 0.235
[28, 180] loss: 0.263
[28, 240] loss: 0.254
[28, 300] loss: 0.263
[28, 360] loss: 0.286
Epoch: 28 -> Loss: 0.269533962011
Epoch: 28 -> Test Accuracy: 85.89
[29, 60] loss: 0.246
[29, 120] loss: 0.257
[29, 180] loss: 0.264
[29, 240] loss: 0.258
[29, 300] loss: 0.266
[29, 360] loss: 0.272
Epoch: 29 -> Loss: 0.421383947134
Epoch: 29 -> Test Accuracy: 85.39
[30, 60] loss: 0.245
[30, 120] loss: 0.251
[30, 180] loss: 0.265
[30, 240] loss: 0.265
[30, 300] loss: 0.281
[30, 360] loss: 0.278
Epoch: 30 -> Loss: 0.290724217892
Epoch: 30 -> Test Accuracy: 85.6
[31, 60] loss: 0.246
[31, 120] loss: 0.255
[31, 180] loss: 0.271
[31, 240] loss: 0.260
[31, 300] loss: 0.265
[31, 360] loss: 0.270
Epoch: 31 -> Loss: 0.24599532783
Epoch: 31 -> Test Accuracy: 86.12
[32, 60] loss: 0.240
[32, 120] loss: 0.257
[32, 180] loss: 0.272
[32, 240] loss: 0.270
[32, 300] loss: 0.250
[32, 360] loss: 0.270
Epoch: 32 -> Loss: 0.198343589902
Epoch: 32 -> Test Accuracy: 86.15
[33, 60] loss: 0.240
[33, 120] loss: 0.254
[33, 180] loss: 0.253
[33, 240] loss: 0.269
[33, 300] loss: 0.279
[33, 360] loss: 0.267
Epoch: 33 -> Loss: 0.337949573994
Epoch: 33 -> Test Accuracy: 85.44
[34, 60] loss: 0.235
[34, 120] loss: 0.241
[34, 180] loss: 0.261
[34, 240] loss: 0.283
[34, 300] loss: 0.272
[34, 360] loss: 0.270
Epoch: 34 -> Loss: 0.290144622326
Epoch: 34 -> Test Accuracy: 86.09
[35, 60] loss: 0.243
[35, 120] loss: 0.235
[35, 180] loss: 0.250
[35, 240] loss: 0.264
[35, 300] loss: 0.283
[35, 360] loss: 0.278
Epoch: 35 -> Loss: 0.235561653972
Epoch: 35 -> Test Accuracy: 86.24
[36, 60] loss: 0.214
[36, 120] loss: 0.166
[36, 180] loss: 0.180
[36, 240] loss: 0.162
[36, 300] loss: 0.161
[36, 360] loss: 0.168
Epoch: 36 -> Loss: 0.140135973692
Epoch: 36 -> Test Accuracy: 88.48
[37, 60] loss: 0.150
[37, 120] loss: 0.148
[37, 180] loss: 0.146
[37, 240] loss: 0.141
[37, 300] loss: 0.141
[37, 360] loss: 0.142
Epoch: 37 -> Loss: 0.127807617188
Epoch: 37 -> Test Accuracy: 88.56
[38, 60] loss: 0.123
[38, 120] loss: 0.130
[38, 180] loss: 0.129
[38, 240] loss: 0.142
[38, 300] loss: 0.135
[38, 360] loss: 0.136
Epoch: 38 -> Loss: 0.172238379717
Epoch: 38 -> Test Accuracy: 88.61
[39, 60] loss: 0.115
[39, 120] loss: 0.118
[39, 180] loss: 0.123
[39, 240] loss: 0.131
[39, 300] loss: 0.131
[39, 360] loss: 0.127
Epoch: 39 -> Loss: 0.13024738431
Epoch: 39 -> Test Accuracy: 88.1
[40, 60] loss: 0.111
[40, 120] loss: 0.110
[40, 180] loss: 0.118
[40, 240] loss: 0.126
[40, 300] loss: 0.123
[40, 360] loss: 0.124
Epoch: 40 -> Loss: 0.0988387987018
Epoch: 40 -> Test Accuracy: 88.52
[41, 60] loss: 0.107
[41, 120] loss: 0.105
[41, 180] loss: 0.115
[41, 240] loss: 0.114
[41, 300] loss: 0.117
[41, 360] loss: 0.126
Epoch: 41 -> Loss: 0.127695798874
Epoch: 41 -> Test Accuracy: 88.43
[42, 60] loss: 0.104
[42, 120] loss: 0.108
[42, 180] loss: 0.103
[42, 240] loss: 0.115
[42, 300] loss: 0.123
[42, 360] loss: 0.124
Epoch: 42 -> Loss: 0.114689387381
Epoch: 42 -> Test Accuracy: 88.14
[43, 60] loss: 0.100
[43, 120] loss: 0.096
[43, 180] loss: 0.104
[43, 240] loss: 0.112
[43, 300] loss: 0.117
[43, 360] loss: 0.118
Epoch: 43 -> Loss: 0.160857588053
Epoch: 43 -> Test Accuracy: 87.7
[44, 60] loss: 0.101
[44, 120] loss: 0.096
[44, 180] loss: 0.106
[44, 240] loss: 0.118
[44, 300] loss: 0.112
[44, 360] loss: 0.113
Epoch: 44 -> Loss: 0.0992850586772
Epoch: 44 -> Test Accuracy: 87.96
[45, 60] loss: 0.090
[45, 120] loss: 0.097
[45, 180] loss: 0.106
[45, 240] loss: 0.107
[45, 300] loss: 0.110
[45, 360] loss: 0.112
Epoch: 45 -> Loss: 0.119655810297
Epoch: 45 -> Test Accuracy: 87.45
[46, 60] loss: 0.097
[46, 120] loss: 0.104
[46, 180] loss: 0.100
[46, 240] loss: 0.118
[46, 300] loss: 0.112
[46, 360] loss: 0.111
Epoch: 46 -> Loss: 0.101536586881
Epoch: 46 -> Test Accuracy: 88.22
[47, 60] loss: 0.090
[47, 120] loss: 0.107
[47, 180] loss: 0.102
[47, 240] loss: 0.106
[47, 300] loss: 0.113
[47, 360] loss: 0.116
Epoch: 47 -> Loss: 0.100572682917
Epoch: 47 -> Test Accuracy: 87.81
[48, 60] loss: 0.104
[48, 120] loss: 0.097
[48, 180] loss: 0.103
[48, 240] loss: 0.100
[48, 300] loss: 0.115
[48, 360] loss: 0.113
Epoch: 48 -> Loss: 0.132347792387
Epoch: 48 -> Test Accuracy: 87.6
[49, 60] loss: 0.096
[49, 120] loss: 0.101
[49, 180] loss: 0.103
[49, 240] loss: 0.113
[49, 300] loss: 0.117
[49, 360] loss: 0.118
Epoch: 49 -> Loss: 0.0958237200975
Epoch: 49 -> Test Accuracy: 88.02
[50, 60] loss: 0.105
[50, 120] loss: 0.101
[50, 180] loss: 0.101
[50, 240] loss: 0.111
[50, 300] loss: 0.107
[50, 360] loss: 0.111
Epoch: 50 -> Loss: 0.12903265655
Epoch: 50 -> Test Accuracy: 87.72
[51, 60] loss: 0.097
[51, 120] loss: 0.106
[51, 180] loss: 0.099
[51, 240] loss: 0.111
[51, 300] loss: 0.100
[51, 360] loss: 0.115
Epoch: 51 -> Loss: 0.153112858534
Epoch: 51 -> Test Accuracy: 87.64
[52, 60] loss: 0.102
[52, 120] loss: 0.103
[52, 180] loss: 0.100
[52, 240] loss: 0.110
[52, 300] loss: 0.114
[52, 360] loss: 0.112
Epoch: 52 -> Loss: 0.112087152898
Epoch: 52 -> Test Accuracy: 87.61
[53, 60] loss: 0.098
[53, 120] loss: 0.099
[53, 180] loss: 0.106
[53, 240] loss: 0.106
[53, 300] loss: 0.118
[53, 360] loss: 0.116
Epoch: 53 -> Loss: 0.17863933742
Epoch: 53 -> Test Accuracy: 87.71
[54, 60] loss: 0.091
[54, 120] loss: 0.107
[54, 180] loss: 0.103
[54, 240] loss: 0.109
[54, 300] loss: 0.109
[54, 360] loss: 0.109
Epoch: 54 -> Loss: 0.0908921808004
Epoch: 54 -> Test Accuracy: 87.58
[55, 60] loss: 0.104
[55, 120] loss: 0.096
[55, 180] loss: 0.103
[55, 240] loss: 0.108
[55, 300] loss: 0.109
[55, 360] loss: 0.116
Epoch: 55 -> Loss: 0.0838604420424
Epoch: 55 -> Test Accuracy: 87.76
[56, 60] loss: 0.092
[56, 120] loss: 0.103
[56, 180] loss: 0.100
[56, 240] loss: 0.110
[56, 300] loss: 0.107
[56, 360] loss: 0.118
Epoch: 56 -> Loss: 0.196120411158
Epoch: 56 -> Test Accuracy: 86.96
[57, 60] loss: 0.103
[57, 120] loss: 0.103
[57, 180] loss: 0.097
[57, 240] loss: 0.109
[57, 300] loss: 0.112
[57, 360] loss: 0.121
Epoch: 57 -> Loss: 0.110311388969
Epoch: 57 -> Test Accuracy: 87.64
[58, 60] loss: 0.095
[58, 120] loss: 0.103
[58, 180] loss: 0.104
[58, 240] loss: 0.109
[58, 300] loss: 0.119
[58, 360] loss: 0.110
Epoch: 58 -> Loss: 0.113412238657
Epoch: 58 -> Test Accuracy: 87.1
[59, 60] loss: 0.108
[59, 120] loss: 0.106
[59, 180] loss: 0.099
[59, 240] loss: 0.106
[59, 300] loss: 0.110
[59, 360] loss: 0.106
Epoch: 59 -> Loss: 0.136997550726
Epoch: 59 -> Test Accuracy: 87.46
[60, 60] loss: 0.098
[60, 120] loss: 0.100
[60, 180] loss: 0.108
[60, 240] loss: 0.103
[60, 300] loss: 0.107
[60, 360] loss: 0.111
Epoch: 60 -> Loss: 0.0945501327515
Epoch: 60 -> Test Accuracy: 87.62
[61, 60] loss: 0.099
[61, 120] loss: 0.099
[61, 180] loss: 0.108
[61, 240] loss: 0.105
[61, 300] loss: 0.116
[61, 360] loss: 0.112
Epoch: 61 -> Loss: 0.141589984298
Epoch: 61 -> Test Accuracy: 86.68
[62, 60] loss: 0.108
[62, 120] loss: 0.100
[62, 180] loss: 0.096
[62, 240] loss: 0.105
[62, 300] loss: 0.106
[62, 360] loss: 0.112
Epoch: 62 -> Loss: 0.12697981298
Epoch: 62 -> Test Accuracy: 87.42
[63, 60] loss: 0.093
[63, 120] loss: 0.095
[63, 180] loss: 0.103
[63, 240] loss: 0.107
[63, 300] loss: 0.105
[63, 360] loss: 0.118
Epoch: 63 -> Loss: 0.105083033442
Epoch: 63 -> Test Accuracy: 87.24
[64, 60] loss: 0.090
[64, 120] loss: 0.096
[64, 180] loss: 0.102
[64, 240] loss: 0.102
[64, 300] loss: 0.105
[64, 360] loss: 0.109
Epoch: 64 -> Loss: 0.0736970603466
Epoch: 64 -> Test Accuracy: 87.56
[65, 60] loss: 0.095
[65, 120] loss: 0.095
[65, 180] loss: 0.094
[65, 240] loss: 0.104
[65, 300] loss: 0.111
[65, 360] loss: 0.108
Epoch: 65 -> Loss: 0.125747174025
Epoch: 65 -> Test Accuracy: 87.38
[66, 60] loss: 0.099
[66, 120] loss: 0.099
[66, 180] loss: 0.097
[66, 240] loss: 0.113
[66, 300] loss: 0.108
[66, 360] loss: 0.115
Epoch: 66 -> Loss: 0.145292937756
Epoch: 66 -> Test Accuracy: 87.94
[67, 60] loss: 0.097
[67, 120] loss: 0.097
[67, 180] loss: 0.101
[67, 240] loss: 0.100
[67, 300] loss: 0.107
[67, 360] loss: 0.109
Epoch: 67 -> Loss: 0.208483934402
Epoch: 67 -> Test Accuracy: 87.41
[68, 60] loss: 0.100
[68, 120] loss: 0.102
[68, 180] loss: 0.103
[68, 240] loss: 0.101
[68, 300] loss: 0.112
[68, 360] loss: 0.105
Epoch: 68 -> Loss: 0.197883352637
Epoch: 68 -> Test Accuracy: 87.42
[69, 60] loss: 0.099
[69, 120] loss: 0.100
[69, 180] loss: 0.097
[69, 240] loss: 0.104
[69, 300] loss: 0.106
[69, 360] loss: 0.115
Epoch: 69 -> Loss: 0.0952011197805
Epoch: 69 -> Test Accuracy: 86.91
[70, 60] loss: 0.094
[70, 120] loss: 0.104
[70, 180] loss: 0.100
[70, 240] loss: 0.104
[70, 300] loss: 0.113
[70, 360] loss: 0.110
Epoch: 70 -> Loss: 0.148934438825
Epoch: 70 -> Test Accuracy: 87.7
[71, 60] loss: 0.078
[71, 120] loss: 0.071
[71, 180] loss: 0.067
[71, 240] loss: 0.061
[71, 300] loss: 0.059
[71, 360] loss: 0.064
Epoch: 71 -> Loss: 0.101838968694
Epoch: 71 -> Test Accuracy: 88.89
[72, 60] loss: 0.054
[72, 120] loss: 0.050
[72, 180] loss: 0.055
[72, 240] loss: 0.055
[72, 300] loss: 0.053
[72, 360] loss: 0.055
Epoch: 72 -> Loss: 0.0629889145494
Epoch: 72 -> Test Accuracy: 88.74
[73, 60] loss: 0.048
[73, 120] loss: 0.048
[73, 180] loss: 0.049
[73, 240] loss: 0.046
[73, 300] loss: 0.050
[73, 360] loss: 0.046
Epoch: 73 -> Loss: 0.0920459479094
Epoch: 73 -> Test Accuracy: 88.85
[74, 60] loss: 0.043
[74, 120] loss: 0.045
[74, 180] loss: 0.045
[74, 240] loss: 0.044
[74, 300] loss: 0.049
[74, 360] loss: 0.042
Epoch: 74 -> Loss: 0.0641450062394
Epoch: 74 -> Test Accuracy: 89.06
[75, 60] loss: 0.040
[75, 120] loss: 0.041
[75, 180] loss: 0.046
[75, 240] loss: 0.043
[75, 300] loss: 0.046
[75, 360] loss: 0.038
Epoch: 75 -> Loss: 0.0862347185612
Epoch: 75 -> Test Accuracy: 88.87
[76, 60] loss: 0.041
[76, 120] loss: 0.040
[76, 180] loss: 0.041
[76, 240] loss: 0.040
[76, 300] loss: 0.043
[76, 360] loss: 0.042
Epoch: 76 -> Loss: 0.031208762899
Epoch: 76 -> Test Accuracy: 88.99
[77, 60] loss: 0.037
[77, 120] loss: 0.036
[77, 180] loss: 0.036
[77, 240] loss: 0.037
[77, 300] loss: 0.039
[77, 360] loss: 0.039
Epoch: 77 -> Loss: 0.0608403757215
Epoch: 77 -> Test Accuracy: 88.89
[78, 60] loss: 0.036
[78, 120] loss: 0.036
[78, 180] loss: 0.038
[78, 240] loss: 0.037
[78, 300] loss: 0.034
[78, 360] loss: 0.041
Epoch: 78 -> Loss: 0.0351943150163
Epoch: 78 -> Test Accuracy: 88.95
[79, 60] loss: 0.034
[79, 120] loss: 0.034
[79, 180] loss: 0.038
[79, 240] loss: 0.040
[79, 300] loss: 0.037
[79, 360] loss: 0.037
Epoch: 79 -> Loss: 0.0699258968234
Epoch: 79 -> Test Accuracy: 89.22
[80, 60] loss: 0.037
[80, 120] loss: 0.032
[80, 180] loss: 0.031
[80, 240] loss: 0.032
[80, 300] loss: 0.035
[80, 360] loss: 0.033
Epoch: 80 -> Loss: 0.0586454160511
Epoch: 80 -> Test Accuracy: 89.04
[81, 60] loss: 0.033
[81, 120] loss: 0.034
[81, 180] loss: 0.034
[81, 240] loss: 0.033
[81, 300] loss: 0.034
[81, 360] loss: 0.033
Epoch: 81 -> Loss: 0.0648641735315
Epoch: 81 -> Test Accuracy: 88.74
[82, 60] loss: 0.030
[82, 120] loss: 0.036
[82, 180] loss: 0.033
[82, 240] loss: 0.035
[82, 300] loss: 0.032
[82, 360] loss: 0.036
Epoch: 82 -> Loss: 0.0335661396384
Epoch: 82 -> Test Accuracy: 88.82
[83, 60] loss: 0.031
[83, 120] loss: 0.030
[83, 180] loss: 0.035
[83, 240] loss: 0.034
[83, 300] loss: 0.035
[83, 360] loss: 0.034
Epoch: 83 -> Loss: 0.0633686929941
Epoch: 83 -> Test Accuracy: 88.84
[84, 60] loss: 0.031
[84, 120] loss: 0.029
[84, 180] loss: 0.031
[84, 240] loss: 0.032
[84, 300] loss: 0.034
[84, 360] loss: 0.032
Epoch: 84 -> Loss: 0.0487174540758
Epoch: 84 -> Test Accuracy: 88.78
[85, 60] loss: 0.030
[85, 120] loss: 0.029
[85, 180] loss: 0.032
[85, 240] loss: 0.030
[85, 300] loss: 0.031
[85, 360] loss: 0.031
Epoch: 85 -> Loss: 0.0835116282105
Epoch: 85 -> Test Accuracy: 88.71
[86, 60] loss: 0.028
[86, 120] loss: 0.029
[86, 180] loss: 0.031
[86, 240] loss: 0.029
[86, 300] loss: 0.027
[86, 360] loss: 0.028
Epoch: 86 -> Loss: 0.023648628965
Epoch: 86 -> Test Accuracy: 88.91
[87, 60] loss: 0.029
[87, 120] loss: 0.031
[87, 180] loss: 0.029
[87, 240] loss: 0.027
[87, 300] loss: 0.026
[87, 360] loss: 0.028
Epoch: 87 -> Loss: 0.0327759198844
Epoch: 87 -> Test Accuracy: 89.0
[88, 60] loss: 0.028
[88, 120] loss: 0.028
[88, 180] loss: 0.026
[88, 240] loss: 0.027
[88, 300] loss: 0.027
[88, 360] loss: 0.028
Epoch: 88 -> Loss: 0.0510449707508
Epoch: 88 -> Test Accuracy: 88.97
[89, 60] loss: 0.026
[89, 120] loss: 0.027
[89, 180] loss: 0.028
[89, 240] loss: 0.025
[89, 300] loss: 0.028
[89, 360] loss: 0.027
Epoch: 89 -> Loss: 0.0274531003088
Epoch: 89 -> Test Accuracy: 89.13
[90, 60] loss: 0.026
[90, 120] loss: 0.028
[90, 180] loss: 0.025
[90, 240] loss: 0.026
[90, 300] loss: 0.026
[90, 360] loss: 0.027
Epoch: 90 -> Loss: 0.0168426446617
Epoch: 90 -> Test Accuracy: 88.93
[91, 60] loss: 0.028
[91, 120] loss: 0.025
[91, 180] loss: 0.026
[91, 240] loss: 0.025
[91, 300] loss: 0.027
[91, 360] loss: 0.029
Epoch: 91 -> Loss: 0.0277911536396
Epoch: 91 -> Test Accuracy: 88.97
[92, 60] loss: 0.029
[92, 120] loss: 0.026
[92, 180] loss: 0.025
[92, 240] loss: 0.026
[92, 300] loss: 0.028
[92, 360] loss: 0.027
Epoch: 92 -> Loss: 0.02224926278
Epoch: 92 -> Test Accuracy: 88.85
[93, 60] loss: 0.025
[93, 120] loss: 0.026
[93, 180] loss: 0.027
[93, 240] loss: 0.025
[93, 300] loss: 0.026
[93, 360] loss: 0.027
Epoch: 93 -> Loss: 0.0203959103674
Epoch: 93 -> Test Accuracy: 88.94
[94, 60] loss: 0.026
[94, 120] loss: 0.027
[94, 180] loss: 0.026
[94, 240] loss: 0.025
[94, 300] loss: 0.025
[94, 360] loss: 0.026
Epoch: 94 -> Loss: 0.0144302491099
Epoch: 94 -> Test Accuracy: 88.98
[95, 60] loss: 0.024
[95, 120] loss: 0.025
[95, 180] loss: 0.027
[95, 240] loss: 0.027
[95, 300] loss: 0.025
[95, 360] loss: 0.025
Epoch: 95 -> Loss: 0.0329773649573
Epoch: 95 -> Test Accuracy: 89.0
[96, 60] loss: 0.026
[96, 120] loss: 0.027
[96, 180] loss: 0.025
[96, 240] loss: 0.024
[96, 300] loss: 0.024
[96, 360] loss: 0.026
Epoch: 96 -> Loss: 0.0315780416131
Epoch: 96 -> Test Accuracy: 89.0
[97, 60] loss: 0.027
[97, 120] loss: 0.027
[97, 180] loss: 0.025
[97, 240] loss: 0.026
[97, 300] loss: 0.026
[97, 360] loss: 0.023
Epoch: 97 -> Loss: 0.0222754776478
Epoch: 97 -> Test Accuracy: 89.04
[98, 60] loss: 0.026
[98, 120] loss: 0.026
[98, 180] loss: 0.025
[98, 240] loss: 0.024
[98, 300] loss: 0.026
[98, 360] loss: 0.028
Epoch: 98 -> Loss: 0.0145429130644
Epoch: 98 -> Test Accuracy: 89.01
[99, 60] loss: 0.024
[99, 120] loss: 0.024
[99, 180] loss: 0.026
[99, 240] loss: 0.023
[99, 300] loss: 0.026
[99, 360] loss: 0.026
Epoch: 99 -> Loss: 0.0245034340769
Epoch: 99 -> Test Accuracy: 88.99
[100, 60] loss: 0.023
[100, 120] loss: 0.024
[100, 180] loss: 0.026
[100, 240] loss: 0.025
[100, 300] loss: 0.023
[100, 360] loss: 0.027
Epoch: 100 -> Loss: 0.01750581339
Epoch: 100 -> Test Accuracy: 88.94
Finished Training
[1, 60] loss: 1.810
[1, 120] loss: 1.521
[1, 180] loss: 1.352
[1, 240] loss: 1.236
[1, 300] loss: 1.153
[1, 360] loss: 1.105
Epoch: 1 -> Loss: 1.12288761139
Epoch: 1 -> Test Accuracy: 61.0
[2, 60] loss: 1.007
[2, 120] loss: 0.947
[2, 180] loss: 0.907
[2, 240] loss: 0.896
[2, 300] loss: 0.846
[2, 360] loss: 0.813
Epoch: 2 -> Loss: 0.656613349915
Epoch: 2 -> Test Accuracy: 70.11
[3, 60] loss: 0.786
[3, 120] loss: 0.770
[3, 180] loss: 0.763
[3, 240] loss: 0.736
[3, 300] loss: 0.731
[3, 360] loss: 0.722
Epoch: 3 -> Loss: 0.771119475365
Epoch: 3 -> Test Accuracy: 72.99
[4, 60] loss: 0.691
[4, 120] loss: 0.696
[4, 180] loss: 0.661
[4, 240] loss: 0.679
[4, 300] loss: 0.662
[4, 360] loss: 0.667
Epoch: 4 -> Loss: 0.700662791729
Epoch: 4 -> Test Accuracy: 73.77
[5, 60] loss: 0.613
[5, 120] loss: 0.643
[5, 180] loss: 0.630
[5, 240] loss: 0.646
[5, 300] loss: 0.628
[5, 360] loss: 0.606
Epoch: 5 -> Loss: 0.581497311592
Epoch: 5 -> Test Accuracy: 76.96
[6, 60] loss: 0.593
[6, 120] loss: 0.573
[6, 180] loss: 0.614
[6, 240] loss: 0.606
[6, 300] loss: 0.609
[6, 360] loss: 0.583
Epoch: 6 -> Loss: 0.478937298059
Epoch: 6 -> Test Accuracy: 78.15
[7, 60] loss: 0.559
[7, 120] loss: 0.586
[7, 180] loss: 0.561
[7, 240] loss: 0.565
[7, 300] loss: 0.570
[7, 360] loss: 0.561
Epoch: 7 -> Loss: 0.330141574144
Epoch: 7 -> Test Accuracy: 78.88
[8, 60] loss: 0.549
[8, 120] loss: 0.544
[8, 180] loss: 0.570
[8, 240] loss: 0.536
[8, 300] loss: 0.545
[8, 360] loss: 0.535
Epoch: 8 -> Loss: 0.493169873953
Epoch: 8 -> Test Accuracy: 78.33
[9, 60] loss: 0.514
[9, 120] loss: 0.529
[9, 180] loss: 0.538
[9, 240] loss: 0.531
[9, 300] loss: 0.518
[9, 360] loss: 0.549
Epoch: 9 -> Loss: 0.515562534332
Epoch: 9 -> Test Accuracy: 80.23
[10, 60] loss: 0.504
[10, 120] loss: 0.511
[10, 180] loss: 0.525
[10, 240] loss: 0.517
[10, 300] loss: 0.501
[10, 360] loss: 0.547
Epoch: 10 -> Loss: 0.511408030987
Epoch: 10 -> Test Accuracy: 81.49
[11, 60] loss: 0.479
[11, 120] loss: 0.500
[11, 180] loss: 0.528
[11, 240] loss: 0.524
[11, 300] loss: 0.506
[11, 360] loss: 0.511
Epoch: 11 -> Loss: 0.641905009747
Epoch: 11 -> Test Accuracy: 80.95
[12, 60] loss: 0.474
[12, 120] loss: 0.513
[12, 180] loss: 0.489
[12, 240] loss: 0.488
[12, 300] loss: 0.496
[12, 360] loss: 0.492
Epoch: 12 -> Loss: 0.483081430197
Epoch: 12 -> Test Accuracy: 79.52
[13, 60] loss: 0.486
[13, 120] loss: 0.490
[13, 180] loss: 0.477
[13, 240] loss: 0.492
[13, 300] loss: 0.500
[13, 360] loss: 0.509
Epoch: 13 -> Loss: 0.418179035187
Epoch: 13 -> Test Accuracy: 80.94
[14, 60] loss: 0.480
[14, 120] loss: 0.457
[14, 180] loss: 0.470
[14, 240] loss: 0.491
[14, 300] loss: 0.487
[14, 360] loss: 0.496
Epoch: 14 -> Loss: 0.465601056814
Epoch: 14 -> Test Accuracy: 81.38
[15, 60] loss: 0.458
[15, 120] loss: 0.475
[15, 180] loss: 0.459
[15, 240] loss: 0.468
[15, 300] loss: 0.476
[15, 360] loss: 0.470
Epoch: 15 -> Loss: 0.502071738243
Epoch: 15 -> Test Accuracy: 80.78
[16, 60] loss: 0.454
[16, 120] loss: 0.455
[16, 180] loss: 0.452
[16, 240] loss: 0.466
[16, 300] loss: 0.470
[16, 360] loss: 0.483
Epoch: 16 -> Loss: 0.539138913155
Epoch: 16 -> Test Accuracy: 83.08
[17, 60] loss: 0.433
[17, 120] loss: 0.466
[17, 180] loss: 0.457
[17, 240] loss: 0.467
[17, 300] loss: 0.456
[17, 360] loss: 0.457
Epoch: 17 -> Loss: 0.438570439816
Epoch: 17 -> Test Accuracy: 82.02
[18, 60] loss: 0.445
[18, 120] loss: 0.459
[18, 180] loss: 0.449
[18, 240] loss: 0.457
[18, 300] loss: 0.474
[18, 360] loss: 0.450
Epoch: 18 -> Loss: 0.288309037685
Epoch: 18 -> Test Accuracy: 81.83
[19, 60] loss: 0.423
[19, 120] loss: 0.457
[19, 180] loss: 0.447
[19, 240] loss: 0.436
[19, 300] loss: 0.455
[19, 360] loss: 0.465
Epoch: 19 -> Loss: 0.568620026112
Epoch: 19 -> Test Accuracy: 81.61
[20, 60] loss: 0.426
[20, 120] loss: 0.443
[20, 180] loss: 0.424
[20, 240] loss: 0.455
[20, 300] loss: 0.458
[20, 360] loss: 0.475
Epoch: 20 -> Loss: 0.423507988453
Epoch: 20 -> Test Accuracy: 82.19
[21, 60] loss: 0.415
[21, 120] loss: 0.432
[21, 180] loss: 0.428
[21, 240] loss: 0.451
[21, 300] loss: 0.453
[21, 360] loss: 0.452
Epoch: 21 -> Loss: 0.330270588398
Epoch: 21 -> Test Accuracy: 83.3
[22, 60] loss: 0.413
[22, 120] loss: 0.413
[22, 180] loss: 0.437
[22, 240] loss: 0.440
[22, 300] loss: 0.432
[22, 360] loss: 0.473
Epoch: 22 -> Loss: 0.324644684792
Epoch: 22 -> Test Accuracy: 83.41
[23, 60] loss: 0.407
[23, 120] loss: 0.437
[23, 180] loss: 0.453
[23, 240] loss: 0.418
[23, 300] loss: 0.440
[23, 360] loss: 0.422
Epoch: 23 -> Loss: 0.340217113495
Epoch: 23 -> Test Accuracy: 82.2
[24, 60] loss: 0.406
[24, 120] loss: 0.439
[24, 180] loss: 0.414
[24, 240] loss: 0.446
[24, 300] loss: 0.440
[24, 360] loss: 0.427
Epoch: 24 -> Loss: 0.404260098934
Epoch: 24 -> Test Accuracy: 82.32
[25, 60] loss: 0.407
[25, 120] loss: 0.424
[25, 180] loss: 0.417
[25, 240] loss: 0.428
[25, 300] loss: 0.420
[25, 360] loss: 0.465
Epoch: 25 -> Loss: 0.548399567604
Epoch: 25 -> Test Accuracy: 81.93
[26, 60] loss: 0.420
[26, 120] loss: 0.395
[26, 180] loss: 0.443
[26, 240] loss: 0.441
[26, 300] loss: 0.440
[26, 360] loss: 0.426
Epoch: 26 -> Loss: 0.495992511511
Epoch: 26 -> Test Accuracy: 81.87
[27, 60] loss: 0.405
[27, 120] loss: 0.429
[27, 180] loss: 0.414
[27, 240] loss: 0.419
[27, 300] loss: 0.452
[27, 360] loss: 0.446
Epoch: 27 -> Loss: 0.423348963261
Epoch: 27 -> Test Accuracy: 83.15
[28, 60] loss: 0.399
[28, 120] loss: 0.402
[28, 180] loss: 0.411
[28, 240] loss: 0.418
[28, 300] loss: 0.431
[28, 360] loss: 0.423
Epoch: 28 -> Loss: 0.42018944025
Epoch: 28 -> Test Accuracy: 83.09
[29, 60] loss: 0.382
[29, 120] loss: 0.425
[29, 180] loss: 0.398
[29, 240] loss: 0.444
[29, 300] loss: 0.424
[29, 360] loss: 0.435
Epoch: 29 -> Loss: 0.463393777609
Epoch: 29 -> Test Accuracy: 81.38
[30, 60] loss: 0.392
[30, 120] loss: 0.412
[30, 180] loss: 0.428
[30, 240] loss: 0.402
[30, 300] loss: 0.423
[30, 360] loss: 0.426
Epoch: 30 -> Loss: 0.498779624701
Epoch: 30 -> Test Accuracy: 82.5
[31, 60] loss: 0.384
[31, 120] loss: 0.398
[31, 180] loss: 0.408
[31, 240] loss: 0.406
[31, 300] loss: 0.422
[31, 360] loss: 0.426
Epoch: 31 -> Loss: 0.430401235819
Epoch: 31 -> Test Accuracy: 82.81
[32, 60] loss: 0.402
[32, 120] loss: 0.413
[32, 180] loss: 0.431
[32, 240] loss: 0.403
[32, 300] loss: 0.411
[32, 360] loss: 0.447
Epoch: 32 -> Loss: 0.481689304113
Epoch: 32 -> Test Accuracy: 83.93
[33, 60] loss: 0.389
[33, 120] loss: 0.419
[33, 180] loss: 0.420
[33, 240] loss: 0.423
[33, 300] loss: 0.413
[33, 360] loss: 0.410
Epoch: 33 -> Loss: 0.509442150593
Epoch: 33 -> Test Accuracy: 82.36
[34, 60] loss: 0.388
[34, 120] loss: 0.387
[34, 180] loss: 0.401
[34, 240] loss: 0.435
[34, 300] loss: 0.441
[34, 360] loss: 0.410
Epoch: 34 -> Loss: 0.367752313614
Epoch: 34 -> Test Accuracy: 83.79
[35, 60] loss: 0.395
[35, 120] loss: 0.405
[35, 180] loss: 0.400
[35, 240] loss: 0.407
[35, 300] loss: 0.398
[35, 360] loss: 0.426
Epoch: 35 -> Loss: 0.374817669392
Epoch: 35 -> Test Accuracy: 83.0
[36, 60] loss: 0.388
[36, 120] loss: 0.399
[36, 180] loss: 0.414
[36, 240] loss: 0.405
[36, 300] loss: 0.434
[36, 360] loss: 0.414
Epoch: 36 -> Loss: 0.409789174795
Epoch: 36 -> Test Accuracy: 84.48
[37, 60] loss: 0.363
[37, 120] loss: 0.391
[37, 180] loss: 0.417
[37, 240] loss: 0.404
[37, 300] loss: 0.431
[37, 360] loss: 0.423
Epoch: 37 -> Loss: 0.465282261372
Epoch: 37 -> Test Accuracy: 82.01
[38, 60] loss: 0.355
[38, 120] loss: 0.406
[38, 180] loss: 0.405
[38, 240] loss: 0.400
[38, 300] loss: 0.407
[38, 360] loss: 0.420
Epoch: 38 -> Loss: 0.604222893715
Epoch: 38 -> Test Accuracy: 82.33
[39, 60] loss: 0.385
[39, 120] loss: 0.390
[39, 180] loss: 0.407
[39, 240] loss: 0.414
[39, 300] loss: 0.409
[39, 360] loss: 0.391
Epoch: 39 -> Loss: 0.261452525854
Epoch: 39 -> Test Accuracy: 83.29
[40, 60] loss: 0.395
[40, 120] loss: 0.381
[40, 180] loss: 0.397
[40, 240] loss: 0.400
[40, 300] loss: 0.411
[40, 360] loss: 0.408
Epoch: 40 -> Loss: 0.410374075174
Epoch: 40 -> Test Accuracy: 80.89
[41, 60] loss: 0.382
[41, 120] loss: 0.387
[41, 180] loss: 0.401
[41, 240] loss: 0.424
[41, 300] loss: 0.399
[41, 360] loss: 0.386
Epoch: 41 -> Loss: 0.302482843399
Epoch: 41 -> Test Accuracy: 84.02
[42, 60] loss: 0.375
[42, 120] loss: 0.383
[42, 180] loss: 0.402
[42, 240] loss: 0.414
[42, 300] loss: 0.410
[42, 360] loss: 0.401
Epoch: 42 -> Loss: 0.545179009438
Epoch: 42 -> Test Accuracy: 83.73
[43, 60] loss: 0.395
[43, 120] loss: 0.394
[43, 180] loss: 0.405
[43, 240] loss: 0.398
[43, 300] loss: 0.403
[43, 360] loss: 0.404
Epoch: 43 -> Loss: 0.430734246969
Epoch: 43 -> Test Accuracy: 83.44
[44, 60] loss: 0.387
[44, 120] loss: 0.382
[44, 180] loss: 0.404
[44, 240] loss: 0.406
[44, 300] loss: 0.398
[44, 360] loss: 0.399
Epoch: 44 -> Loss: 0.45417919755
Epoch: 44 -> Test Accuracy: 83.89
[45, 60] loss: 0.383
[45, 120] loss: 0.388
[45, 180] loss: 0.388
[45, 240] loss: 0.388
[45, 300] loss: 0.391
[45, 360] loss: 0.427
Epoch: 45 -> Loss: 0.483777672052
Epoch: 45 -> Test Accuracy: 83.69
[46, 60] loss: 0.352
[46, 120] loss: 0.418
[46, 180] loss: 0.401
[46, 240] loss: 0.402
[46, 300] loss: 0.406
[46, 360] loss: 0.397
Epoch: 46 -> Loss: 0.354863882065
Epoch: 46 -> Test Accuracy: 84.82
[47, 60] loss: 0.363
[47, 120] loss: 0.398
[47, 180] loss: 0.382
[47, 240] loss: 0.391
[47, 300] loss: 0.427
[47, 360] loss: 0.410
Epoch: 47 -> Loss: 0.375812649727
Epoch: 47 -> Test Accuracy: 82.92
[48, 60] loss: 0.392
[48, 120] loss: 0.382
[48, 180] loss: 0.381
[48, 240] loss: 0.404
[48, 300] loss: 0.388
[48, 360] loss: 0.397
Epoch: 48 -> Loss: 0.652660787106
Epoch: 48 -> Test Accuracy: 83.91
[49, 60] loss: 0.382
[49, 120] loss: 0.385
[49, 180] loss: 0.371
[49, 240] loss: 0.401
[49, 300] loss: 0.417
[49, 360] loss: 0.412
Epoch: 49 -> Loss: 0.257808864117
Epoch: 49 -> Test Accuracy: 82.13
[50, 60] loss: 0.394
[50, 120] loss: 0.372
[50, 180] loss: 0.386
[50, 240] loss: 0.391
[50, 300] loss: 0.401
[50, 360] loss: 0.419
Epoch: 50 -> Loss: 0.4174708426
Epoch: 50 -> Test Accuracy: 83.12
[51, 60] loss: 0.362
[51, 120] loss: 0.365
[51, 180] loss: 0.397
[51, 240] loss: 0.401
[51, 300] loss: 0.419
[51, 360] loss: 0.415
Epoch: 51 -> Loss: 0.376387149096
Epoch: 51 -> Test Accuracy: 83.89
[52, 60] loss: 0.373
[52, 120] loss: 0.382
[52, 180] loss: 0.377
[52, 240] loss: 0.387
[52, 300] loss: 0.410
[52, 360] loss: 0.402
Epoch: 52 -> Loss: 0.488904893398
Epoch: 52 -> Test Accuracy: 84.17
[53, 60] loss: 0.369
[53, 120] loss: 0.386
[53, 180] loss: 0.395
[53, 240] loss: 0.396
[53, 300] loss: 0.393
[53, 360] loss: 0.401
Epoch: 53 -> Loss: 0.561681389809
Epoch: 53 -> Test Accuracy: 84.35
[54, 60] loss: 0.378
[54, 120] loss: 0.364
[54, 180] loss: 0.379
[54, 240] loss: 0.399
[54, 300] loss: 0.406
[54, 360] loss: 0.398
Epoch: 54 -> Loss: 0.377320796251
Epoch: 54 -> Test Accuracy: 83.94
[55, 60] loss: 0.352
[55, 120] loss: 0.368
[55, 180] loss: 0.396
[55, 240] loss: 0.395
[55, 300] loss: 0.396
[55, 360] loss: 0.403
Epoch: 55 -> Loss: 0.412592083216
Epoch: 55 -> Test Accuracy: 82.49
[56, 60] loss: 0.387
[56, 120] loss: 0.381
[56, 180] loss: 0.370
[56, 240] loss: 0.395
[56, 300] loss: 0.391
[56, 360] loss: 0.405
Epoch: 56 -> Loss: 0.258317321539
Epoch: 56 -> Test Accuracy: 83.6
[57, 60] loss: 0.376
[57, 120] loss: 0.376
[57, 180] loss: 0.408
[57, 240] loss: 0.391
[57, 300] loss: 0.386
[57, 360] loss: 0.380
Epoch: 57 -> Loss: 0.580971181393
Epoch: 57 -> Test Accuracy: 83.28
[58, 60] loss: 0.368
[58, 120] loss: 0.369
[58, 180] loss: 0.395
[58, 240] loss: 0.386
[58, 300] loss: 0.411
[58, 360] loss: 0.394
Epoch: 58 -> Loss: 0.397532403469
Epoch: 58 -> Test Accuracy: 85.16
[59, 60] loss: 0.382
[59, 120] loss: 0.384
[59, 180] loss: 0.387
[59, 240] loss: 0.392
[59, 300] loss: 0.398
[59, 360] loss: 0.390
Epoch: 59 -> Loss: 0.393878996372
Epoch: 59 -> Test Accuracy: 83.21
[60, 60] loss: 0.351
[60, 120] loss: 0.388
[60, 180] loss: 0.399
[60, 240] loss: 0.365
[60, 300] loss: 0.380
[60, 360] loss: 0.395
Epoch: 60 -> Loss: 0.27115380764
Epoch: 60 -> Test Accuracy: 84.06
[61, 60] loss: 0.271
[61, 120] loss: 0.225
[61, 180] loss: 0.204
[61, 240] loss: 0.215
[61, 300] loss: 0.201
[61, 360] loss: 0.217
Epoch: 61 -> Loss: 0.184683158994
Epoch: 61 -> Test Accuracy: 89.15
[62, 60] loss: 0.172
[62, 120] loss: 0.175
[62, 180] loss: 0.182
[62, 240] loss: 0.175
[62, 300] loss: 0.184
[62, 360] loss: 0.178
Epoch: 62 -> Loss: 0.247966215014
Epoch: 62 -> Test Accuracy: 89.64
[63, 60] loss: 0.156
[63, 120] loss: 0.147
[63, 180] loss: 0.153
[63, 240] loss: 0.175
[63, 300] loss: 0.172
[63, 360] loss: 0.171
Epoch: 63 -> Loss: 0.0811947211623
Epoch: 63 -> Test Accuracy: 89.98
[64, 60] loss: 0.147
[64, 120] loss: 0.145
[64, 180] loss: 0.159
[64, 240] loss: 0.154
[64, 300] loss: 0.152
[64, 360] loss: 0.158
Epoch: 64 -> Loss: 0.163954615593
Epoch: 64 -> Test Accuracy: 89.41
[65, 60] loss: 0.139
[65, 120] loss: 0.133
[65, 180] loss: 0.145
[65, 240] loss: 0.143
[65, 300] loss: 0.140
[65, 360] loss: 0.143
Epoch: 65 -> Loss: 0.124986365438
Epoch: 65 -> Test Accuracy: 88.97
[66, 60] loss: 0.129
[66, 120] loss: 0.129
[66, 180] loss: 0.133
[66, 240] loss: 0.142
[66, 300] loss: 0.143
[66, 360] loss: 0.143
Epoch: 66 -> Loss: 0.185143619776
Epoch: 66 -> Test Accuracy: 88.99
[67, 60] loss: 0.122
[67, 120] loss: 0.117
[67, 180] loss: 0.123
[67, 240] loss: 0.140
[67, 300] loss: 0.135
[67, 360] loss: 0.134
Epoch: 67 -> Loss: 0.167147248983
Epoch: 67 -> Test Accuracy: 88.84
[68, 60] loss: 0.121
[68, 120] loss: 0.123
[68, 180] loss: 0.155
[68, 240] loss: 0.147
[68, 300] loss: 0.140
[68, 360] loss: 0.146
Epoch: 68 -> Loss: 0.0710888057947
Epoch: 68 -> Test Accuracy: 89.42
[69, 60] loss: 0.118
[69, 120] loss: 0.127
[69, 180] loss: 0.126
[69, 240] loss: 0.134
[69, 300] loss: 0.139
[69, 360] loss: 0.142
Epoch: 69 -> Loss: 0.240266010165
Epoch: 69 -> Test Accuracy: 89.55
[70, 60] loss: 0.129
[70, 120] loss: 0.126
[70, 180] loss: 0.134
[70, 240] loss: 0.134
[70, 300] loss: 0.148
[70, 360] loss: 0.149
Epoch: 70 -> Loss: 0.102905832231
Epoch: 70 -> Test Accuracy: 88.61
[71, 60] loss: 0.128
[71, 120] loss: 0.133
[71, 180] loss: 0.125
[71, 240] loss: 0.132
[71, 300] loss: 0.140
[71, 360] loss: 0.145
Epoch: 71 -> Loss: 0.230306059122
Epoch: 71 -> Test Accuracy: 88.57
[72, 60] loss: 0.121
[72, 120] loss: 0.127
[72, 180] loss: 0.130
[72, 240] loss: 0.141
[72, 300] loss: 0.144
[72, 360] loss: 0.155
Epoch: 72 -> Loss: 0.207720682025
Epoch: 72 -> Test Accuracy: 88.41
[73, 60] loss: 0.142
[73, 120] loss: 0.131
[73, 180] loss: 0.137
[73, 240] loss: 0.133
[73, 300] loss: 0.136
[73, 360] loss: 0.149
Epoch: 73 -> Loss: 0.142775982618
Epoch: 73 -> Test Accuracy: 87.91
[74, 60] loss: 0.126
[74, 120] loss: 0.121
[74, 180] loss: 0.130
[74, 240] loss: 0.148
[74, 300] loss: 0.136
[74, 360] loss: 0.143
Epoch: 74 -> Loss: 0.116629101336
Epoch: 74 -> Test Accuracy: 88.44
[75, 60] loss: 0.127
[75, 120] loss: 0.136
[75, 180] loss: 0.136
[75, 240] loss: 0.148
[75, 300] loss: 0.148
[75, 360] loss: 0.149
Epoch: 75 -> Loss: 0.0989060997963
Epoch: 75 -> Test Accuracy: 87.99
[76, 60] loss: 0.121
[76, 120] loss: 0.126
[76, 180] loss: 0.124
[76, 240] loss: 0.135
[76, 300] loss: 0.149
[76, 360] loss: 0.152
Epoch: 76 -> Loss: 0.109110489488
Epoch: 76 -> Test Accuracy: 87.75
[77, 60] loss: 0.135
[77, 120] loss: 0.132
[77, 180] loss: 0.126
[77, 240] loss: 0.140
[77, 300] loss: 0.142
[77, 360] loss: 0.146
Epoch: 77 -> Loss: 0.111236132681
Epoch: 77 -> Test Accuracy: 88.7
[78, 60] loss: 0.130
[78, 120] loss: 0.136
[78, 180] loss: 0.130
[78, 240] loss: 0.146
[78, 300] loss: 0.141
[78, 360] loss: 0.147
Epoch: 78 -> Loss: 0.200542613864
Epoch: 78 -> Test Accuracy: 88.6
[79, 60] loss: 0.139
[79, 120] loss: 0.129
[79, 180] loss: 0.138
[79, 240] loss: 0.141
[79, 300] loss: 0.148
[79, 360] loss: 0.146
Epoch: 79 -> Loss: 0.094291254878
Epoch: 79 -> Test Accuracy: 88.41
[80, 60] loss: 0.129
[80, 120] loss: 0.130
[80, 180] loss: 0.134
[80, 240] loss: 0.139
[80, 300] loss: 0.148
[80, 360] loss: 0.146
Epoch: 80 -> Loss: 0.173632472754
Epoch: 80 -> Test Accuracy: 88.76
[81, 60] loss: 0.126
[81, 120] loss: 0.128
[81, 180] loss: 0.137
[81, 240] loss: 0.136
[81, 300] loss: 0.144
[81, 360] loss: 0.152
Epoch: 81 -> Loss: 0.279099166393
Epoch: 81 -> Test Accuracy: 89.16
[82, 60] loss: 0.138
[82, 120] loss: 0.142
[82, 180] loss: 0.152
[82, 240] loss: 0.130
[82, 300] loss: 0.152
[82, 360] loss: 0.151
Epoch: 82 -> Loss: 0.135829001665
Epoch: 82 -> Test Accuracy: 88.82
[83, 60] loss: 0.123
[83, 120] loss: 0.119
[83, 180] loss: 0.134
[83, 240] loss: 0.147
[83, 300] loss: 0.146
[83, 360] loss: 0.149
Epoch: 83 -> Loss: 0.142142474651
Epoch: 83 -> Test Accuracy: 88.2
[84, 60] loss: 0.112
[84, 120] loss: 0.137
[84, 180] loss: 0.128
[84, 240] loss: 0.138
[84, 300] loss: 0.141
[84, 360] loss: 0.153
Epoch: 84 -> Loss: 0.196907579899
Epoch: 84 -> Test Accuracy: 88.01
[85, 60] loss: 0.135
[85, 120] loss: 0.132
[85, 180] loss: 0.118
[85, 240] loss: 0.126
[85, 300] loss: 0.142
[85, 360] loss: 0.141
Epoch: 85 -> Loss: 0.189886763692
Epoch: 85 -> Test Accuracy: 87.82
[86, 60] loss: 0.125
[86, 120] loss: 0.133
[86, 180] loss: 0.132
[86, 240] loss: 0.132
[86, 300] loss: 0.149
[86, 360] loss: 0.150
Epoch: 86 -> Loss: 0.148542538285
Epoch: 86 -> Test Accuracy: 89.02
[87, 60] loss: 0.127
[87, 120] loss: 0.126
[87, 180] loss: 0.139
[87, 240] loss: 0.131
[87, 300] loss: 0.143
[87, 360] loss: 0.146
Epoch: 87 -> Loss: 0.172487735748
Epoch: 87 -> Test Accuracy: 88.54
[88, 60] loss: 0.124
[88, 120] loss: 0.120
[88, 180] loss: 0.147
[88, 240] loss: 0.134
[88, 300] loss: 0.137
[88, 360] loss: 0.132
Epoch: 88 -> Loss: 0.147699654102
Epoch: 88 -> Test Accuracy: 88.09
[89, 60] loss: 0.125
[89, 120] loss: 0.127
[89, 180] loss: 0.139
[89, 240] loss: 0.138
[89, 300] loss: 0.144
[89, 360] loss: 0.151
Epoch: 89 -> Loss: 0.229477882385
Epoch: 89 -> Test Accuracy: 87.65
[90, 60] loss: 0.136
[90, 120] loss: 0.128
[90, 180] loss: 0.129
[90, 240] loss: 0.131
[90, 300] loss: 0.139
[90, 360] loss: 0.141
Epoch: 90 -> Loss: 0.19520072639
Epoch: 90 -> Test Accuracy: 87.86
[91, 60] loss: 0.124
[91, 120] loss: 0.127
[91, 180] loss: 0.127
[91, 240] loss: 0.134
[91, 300] loss: 0.149
[91, 360] loss: 0.148
Epoch: 91 -> Loss: 0.2024397403
Epoch: 91 -> Test Accuracy: 88.52
[92, 60] loss: 0.120
[92, 120] loss: 0.122
[92, 180] loss: 0.122
[92, 240] loss: 0.127
[92, 300] loss: 0.140
[92, 360] loss: 0.151
Epoch: 92 -> Loss: 0.0761212706566
Epoch: 92 -> Test Accuracy: 88.65
[93, 60] loss: 0.118
[93, 120] loss: 0.113
[93, 180] loss: 0.133
[93, 240] loss: 0.131
[93, 300] loss: 0.141
[93, 360] loss: 0.150
Epoch: 93 -> Loss: 0.145360976458
Epoch: 93 -> Test Accuracy: 88.7
[94, 60] loss: 0.112
[94, 120] loss: 0.119
[94, 180] loss: 0.124
[94, 240] loss: 0.142
[94, 300] loss: 0.141
[94, 360] loss: 0.133
Epoch: 94 -> Loss: 0.166016787291
Epoch: 94 -> Test Accuracy: 88.45
[95, 60] loss: 0.114
[95, 120] loss: 0.124
[95, 180] loss: 0.118
[95, 240] loss: 0.137
[95, 300] loss: 0.141
[95, 360] loss: 0.146
Epoch: 95 -> Loss: 0.102204702795
Epoch: 95 -> Test Accuracy: 88.71
[96, 60] loss: 0.122
[96, 120] loss: 0.123
[96, 180] loss: 0.129
[96, 240] loss: 0.122
[96, 300] loss: 0.133
[96, 360] loss: 0.133
Epoch: 96 -> Loss: 0.108994938433
Epoch: 96 -> Test Accuracy: 88.34
[97, 60] loss: 0.122
[97, 120] loss: 0.121
[97, 180] loss: 0.125
[97, 240] loss: 0.113
[97, 300] loss: 0.139
[97, 360] loss: 0.137
Epoch: 97 -> Loss: 0.210184052587
Epoch: 97 -> Test Accuracy: 88.2
[98, 60] loss: 0.109
[98, 120] loss: 0.115
[98, 180] loss: 0.116
[98, 240] loss: 0.127
[98, 300] loss: 0.124
[98, 360] loss: 0.137
Epoch: 98 -> Loss: 0.177640527487
Epoch: 98 -> Test Accuracy: 88.43
[99, 60] loss: 0.118
[99, 120] loss: 0.105
[99, 180] loss: 0.123
[99, 240] loss: 0.127
[99, 300] loss: 0.134
[99, 360] loss: 0.144
Epoch: 99 -> Loss: 0.103200897574
Epoch: 99 -> Test Accuracy: 88.29
[100, 60] loss: 0.107
[100, 120] loss: 0.122
[100, 180] loss: 0.119
[100, 240] loss: 0.125
[100, 300] loss: 0.131
[100, 360] loss: 0.140
Epoch: 100 -> Loss: 0.155915349722
Epoch: 100 -> Test Accuracy: 88.03
[101, 60] loss: 0.123
[101, 120] loss: 0.131
[101, 180] loss: 0.131
[101, 240] loss: 0.125
[101, 300] loss: 0.131
[101, 360] loss: 0.165
Epoch: 101 -> Loss: 0.110289469361
Epoch: 101 -> Test Accuracy: 88.01
[102, 60] loss: 0.115
[102, 120] loss: 0.118
[102, 180] loss: 0.120
[102, 240] loss: 0.133
[102, 300] loss: 0.137
[102, 360] loss: 0.136
Epoch: 102 -> Loss: 0.199165135622
Epoch: 102 -> Test Accuracy: 88.09
[103, 60] loss: 0.103
[103, 120] loss: 0.120
[103, 180] loss: 0.130
[103, 240] loss: 0.136
[103, 300] loss: 0.129
[103, 360] loss: 0.131
Epoch: 103 -> Loss: 0.169885784388
Epoch: 103 -> Test Accuracy: 88.94
[104, 60] loss: 0.115
[104, 120] loss: 0.121
[104, 180] loss: 0.128
[104, 240] loss: 0.120
[104, 300] loss: 0.126
[104, 360] loss: 0.130
Epoch: 104 -> Loss: 0.119063951075
Epoch: 104 -> Test Accuracy: 88.54
[105, 60] loss: 0.115
[105, 120] loss: 0.121
[105, 180] loss: 0.123
[105, 240] loss: 0.124
[105, 300] loss: 0.127
[105, 360] loss: 0.135
Epoch: 105 -> Loss: 0.120506562293
Epoch: 105 -> Test Accuracy: 88.43
[106, 60] loss: 0.123
[106, 120] loss: 0.112
[106, 180] loss: 0.117
[106, 240] loss: 0.134
[106, 300] loss: 0.115
[106, 360] loss: 0.131
Epoch: 106 -> Loss: 0.187677174807
Epoch: 106 -> Test Accuracy: 88.6
[107, 60] loss: 0.112
[107, 120] loss: 0.114
[107, 180] loss: 0.127
[107, 240] loss: 0.118
[107, 300] loss: 0.138
[107, 360] loss: 0.133
Epoch: 107 -> Loss: 0.0318616889417
Epoch: 107 -> Test Accuracy: 87.91
[108, 60] loss: 0.105
[108, 120] loss: 0.111
[108, 180] loss: 0.112
[108, 240] loss: 0.131
[108, 300] loss: 0.140
[108, 360] loss: 0.138
Epoch: 108 -> Loss: 0.160463631153
Epoch: 108 -> Test Accuracy: 88.21
[109, 60] loss: 0.125
[109, 120] loss: 0.117
[109, 180] loss: 0.120
[109, 240] loss: 0.115
[109, 300] loss: 0.119
[109, 360] loss: 0.138
Epoch: 109 -> Loss: 0.112678147852
Epoch: 109 -> Test Accuracy: 88.54
[110, 60] loss: 0.114
[110, 120] loss: 0.103
[110, 180] loss: 0.112
[110, 240] loss: 0.125
[110, 300] loss: 0.137
[110, 360] loss: 0.136
Epoch: 110 -> Loss: 0.152882963419
Epoch: 110 -> Test Accuracy: 89.04
[111, 60] loss: 0.118
[111, 120] loss: 0.113
[111, 180] loss: 0.120
[111, 240] loss: 0.125
[111, 300] loss: 0.121
[111, 360] loss: 0.118
Epoch: 111 -> Loss: 0.0996031239629
Epoch: 111 -> Test Accuracy: 87.94
[112, 60] loss: 0.115
[112, 120] loss: 0.117
[112, 180] loss: 0.127
[112, 240] loss: 0.123
[112, 300] loss: 0.128
[112, 360] loss: 0.133
Epoch: 112 -> Loss: 0.166124328971
Epoch: 112 -> Test Accuracy: 87.92
[113, 60] loss: 0.107
[113, 120] loss: 0.117
[113, 180] loss: 0.114
[113, 240] loss: 0.117
[113, 300] loss: 0.119
[113, 360] loss: 0.135
Epoch: 113 -> Loss: 0.140588134527
Epoch: 113 -> Test Accuracy: 88.48
[114, 60] loss: 0.107
[114, 120] loss: 0.114
[114, 180] loss: 0.111
[114, 240] loss: 0.121
[114, 300] loss: 0.130
[114, 360] loss: 0.128
Epoch: 114 -> Loss: 0.212688833475
Epoch: 114 -> Test Accuracy: 88.04
[115, 60] loss: 0.108
[115, 120] loss: 0.117
[115, 180] loss: 0.123
[115, 240] loss: 0.130
[115, 300] loss: 0.123
[115, 360] loss: 0.138
Epoch: 115 -> Loss: 0.135155707598
Epoch: 115 -> Test Accuracy: 87.29
[116, 60] loss: 0.119
[116, 120] loss: 0.105
[116, 180] loss: 0.111
[116, 240] loss: 0.118
[116, 300] loss: 0.125
[116, 360] loss: 0.126
Epoch: 116 -> Loss: 0.0527365282178
Epoch: 116 -> Test Accuracy: 88.56
[117, 60] loss: 0.107
[117, 120] loss: 0.105
[117, 180] loss: 0.117
[117, 240] loss: 0.117
[117, 300] loss: 0.129
[117, 360] loss: 0.120
Epoch: 117 -> Loss: 0.0917150899768
Epoch: 117 -> Test Accuracy: 88.36
[118, 60] loss: 0.097
[118, 120] loss: 0.111
[118, 180] loss: 0.117
[118, 240] loss: 0.118
[118, 300] loss: 0.138
[118, 360] loss: 0.146
Epoch: 118 -> Loss: 0.15217718482
Epoch: 118 -> Test Accuracy: 87.54
[119, 60] loss: 0.115
[119, 120] loss: 0.114
[119, 180] loss: 0.115
[119, 240] loss: 0.118
[119, 300] loss: 0.118
[119, 360] loss: 0.132
Epoch: 119 -> Loss: 0.176103949547
Epoch: 119 -> Test Accuracy: 88.85
[120, 60] loss: 0.109
[120, 120] loss: 0.114
[120, 180] loss: 0.112
[120, 240] loss: 0.122
[120, 300] loss: 0.133
[120, 360] loss: 0.116
Epoch: 120 -> Loss: 0.0617025382817
Epoch: 120 -> Test Accuracy: 88.76
[121, 60] loss: 0.070
[121, 120] loss: 0.050
[121, 180] loss: 0.046
[121, 240] loss: 0.047
[121, 300] loss: 0.040
[121, 360] loss: 0.041
Epoch: 121 -> Loss: 0.0158089995384
Epoch: 121 -> Test Accuracy: 91.12
[122, 60] loss: 0.034
[122, 120] loss: 0.029
[122, 180] loss: 0.032
[122, 240] loss: 0.032
[122, 300] loss: 0.029
[122, 360] loss: 0.034
Epoch: 122 -> Loss: 0.0332125239074
Epoch: 122 -> Test Accuracy: 91.09
[123, 60] loss: 0.028
[123, 120] loss: 0.024
[123, 180] loss: 0.027
[123, 240] loss: 0.025
[123, 300] loss: 0.027
[123, 360] loss: 0.025
Epoch: 123 -> Loss: 0.0342334732413
Epoch: 123 -> Test Accuracy: 91.33
[124, 60] loss: 0.024
[124, 120] loss: 0.022
[124, 180] loss: 0.025
[124, 240] loss: 0.024
[124, 300] loss: 0.024
[124, 360] loss: 0.023
Epoch: 124 -> Loss: 0.0307826045901
Epoch: 124 -> Test Accuracy: 91.4
[125, 60] loss: 0.021
[125, 120] loss: 0.022
[125, 180] loss: 0.021
[125, 240] loss: 0.021
[125, 300] loss: 0.020
[125, 360] loss: 0.021
Epoch: 125 -> Loss: 0.00836864393204
Epoch: 125 -> Test Accuracy: 91.36
[126, 60] loss: 0.020
[126, 120] loss: 0.018
[126, 180] loss: 0.018
[126, 240] loss: 0.020
[126, 300] loss: 0.020
[126, 360] loss: 0.020
Epoch: 126 -> Loss: 0.018958395347
Epoch: 126 -> Test Accuracy: 91.32
[127, 60] loss: 0.016
[127, 120] loss: 0.017
[127, 180] loss: 0.019
[127, 240] loss: 0.019
[127, 300] loss: 0.020
[127, 360] loss: 0.019
Epoch: 127 -> Loss: 0.0133167505264
Epoch: 127 -> Test Accuracy: 91.38
[128, 60] loss: 0.017
[128, 120] loss: 0.014
[128, 180] loss: 0.016
[128, 240] loss: 0.016
[128, 300] loss: 0.017
[128, 360] loss: 0.017
Epoch: 128 -> Loss: 0.0116452155635
Epoch: 128 -> Test Accuracy: 91.46
[129, 60] loss: 0.015
[129, 120] loss: 0.015
[129, 180] loss: 0.015
[129, 240] loss: 0.014
[129, 300] loss: 0.016
[129, 360] loss: 0.018
Epoch: 129 -> Loss: 0.0130935786292
Epoch: 129 -> Test Accuracy: 91.05
[130, 60] loss: 0.015
[130, 120] loss: 0.013
[130, 180] loss: 0.015
[130, 240] loss: 0.015
[130, 300] loss: 0.015
[130, 360] loss: 0.016
Epoch: 130 -> Loss: 0.00634835381061
Epoch: 130 -> Test Accuracy: 91.38
[131, 60] loss: 0.014
[131, 120] loss: 0.015
[131, 180] loss: 0.014
[131, 240] loss: 0.016
[131, 300] loss: 0.016
[131, 360] loss: 0.014
Epoch: 131 -> Loss: 0.021570796147
Epoch: 131 -> Test Accuracy: 91.49
[132, 60] loss: 0.013
[132, 120] loss: 0.014
[132, 180] loss: 0.014
[132, 240] loss: 0.015
[132, 300] loss: 0.014
[132, 360] loss: 0.013
Epoch: 132 -> Loss: 0.00960294343531
Epoch: 132 -> Test Accuracy: 91.59
[133, 60] loss: 0.013
[133, 120] loss: 0.015
[133, 180] loss: 0.015
[133, 240] loss: 0.014
[133, 300] loss: 0.015
[133, 360] loss: 0.015
Epoch: 133 -> Loss: 0.0142041146755
Epoch: 133 -> Test Accuracy: 91.4
[134, 60] loss: 0.012
[134, 120] loss: 0.012
[134, 180] loss: 0.014
[134, 240] loss: 0.013
[134, 300] loss: 0.013
[134, 360] loss: 0.013
Epoch: 134 -> Loss: 0.00717277545482
Epoch: 134 -> Test Accuracy: 91.26
[135, 60] loss: 0.014
[135, 120] loss: 0.014
[135, 180] loss: 0.014
[135, 240] loss: 0.012
[135, 300] loss: 0.012
[135, 360] loss: 0.012
Epoch: 135 -> Loss: 0.0177748799324
Epoch: 135 -> Test Accuracy: 91.39
[136, 60] loss: 0.011
[136, 120] loss: 0.013
[136, 180] loss: 0.013
[136, 240] loss: 0.015
[136, 300] loss: 0.012
[136, 360] loss: 0.012
Epoch: 136 -> Loss: 0.00705651659518
Epoch: 136 -> Test Accuracy: 91.33
[137, 60] loss: 0.011
[137, 120] loss: 0.014
[137, 180] loss: 0.013
[137, 240] loss: 0.013
[137, 300] loss: 0.012
[137, 360] loss: 0.012
Epoch: 137 -> Loss: 0.0143475178629
Epoch: 137 -> Test Accuracy: 91.21
[138, 60] loss: 0.011
[138, 120] loss: 0.012
[138, 180] loss: 0.013
[138, 240] loss: 0.012
[138, 300] loss: 0.012
[138, 360] loss: 0.013
Epoch: 138 -> Loss: 0.0162122733891
Epoch: 138 -> Test Accuracy: 91.4
[139, 60] loss: 0.011
[139, 120] loss: 0.011
[139, 180] loss: 0.011
[139, 240] loss: 0.012
[139, 300] loss: 0.012
[139, 360] loss: 0.011
Epoch: 139 -> Loss: 0.01758239232
Epoch: 139 -> Test Accuracy: 91.37
[140, 60] loss: 0.010
[140, 120] loss: 0.011
[140, 180] loss: 0.011
[140, 240] loss: 0.012
[140, 300] loss: 0.010
[140, 360] loss: 0.012
Epoch: 140 -> Loss: 0.0165322963148
Epoch: 140 -> Test Accuracy: 91.22
[141, 60] loss: 0.011
[141, 120] loss: 0.011
[141, 180] loss: 0.011
[141, 240] loss: 0.012
[141, 300] loss: 0.010
[141, 360] loss: 0.010
Epoch: 141 -> Loss: 0.00455831876025
Epoch: 141 -> Test Accuracy: 91.24
[142, 60] loss: 0.010
[142, 120] loss: 0.011
[142, 180] loss: 0.010
[142, 240] loss: 0.011
[142, 300] loss: 0.011
[142, 360] loss: 0.012
Epoch: 142 -> Loss: 0.0214500371367
Epoch: 142 -> Test Accuracy: 91.4
[143, 60] loss: 0.010
[143, 120] loss: 0.009
[143, 180] loss: 0.010
[143, 240] loss: 0.010
[143, 300] loss: 0.011
[143, 360] loss: 0.011
Epoch: 143 -> Loss: 0.0189341306686
Epoch: 143 -> Test Accuracy: 91.35
[144, 60] loss: 0.010
[144, 120] loss: 0.010
[144, 180] loss: 0.011
[144, 240] loss: 0.011
[144, 300] loss: 0.010
[144, 360] loss: 0.011
Epoch: 144 -> Loss: 0.0113361775875
Epoch: 144 -> Test Accuracy: 91.29
[145, 60] loss: 0.011
[145, 120] loss: 0.010
[145, 180] loss: 0.009
[145, 240] loss: 0.011
[145, 300] loss: 0.010
[145, 360] loss: 0.010
Epoch: 145 -> Loss: 0.00607524532825
Epoch: 145 -> Test Accuracy: 91.31
[146, 60] loss: 0.010
[146, 120] loss: 0.011
[146, 180] loss: 0.010
[146, 240] loss: 0.010
[146, 300] loss: 0.010
[146, 360] loss: 0.010
Epoch: 146 -> Loss: 0.00923008285463
Epoch: 146 -> Test Accuracy: 91.2
[147, 60] loss: 0.009
[147, 120] loss: 0.010
[147, 180] loss: 0.011
[147, 240] loss: 0.011
[147, 300] loss: 0.010
[147, 360] loss: 0.011
Epoch: 147 -> Loss: 0.00989549141377
Epoch: 147 -> Test Accuracy: 91.22
[148, 60] loss: 0.009
[148, 120] loss: 0.010
[148, 180] loss: 0.010
[148, 240] loss: 0.011
[148, 300] loss: 0.011
[148, 360] loss: 0.011
Epoch: 148 -> Loss: 0.00719866156578
Epoch: 148 -> Test Accuracy: 91.43
[149, 60] loss: 0.011
[149, 120] loss: 0.010
[149, 180] loss: 0.010
[149, 240] loss: 0.010
[149, 300] loss: 0.009
[149, 360] loss: 0.010
Epoch: 149 -> Loss: 0.0087026655674
Epoch: 149 -> Test Accuracy: 91.37
[150, 60] loss: 0.009
[150, 120] loss: 0.010
[150, 180] loss: 0.010
[150, 240] loss: 0.011
[150, 300] loss: 0.011
[150, 360] loss: 0.011
Epoch: 150 -> Loss: 0.0234594587237
Epoch: 150 -> Test Accuracy: 91.19
[151, 60] loss: 0.010
[151, 120] loss: 0.011
[151, 180] loss: 0.010
[151, 240] loss: 0.010
[151, 300] loss: 0.010
[151, 360] loss: 0.011
Epoch: 151 -> Loss: 0.0275905188173
Epoch: 151 -> Test Accuracy: 91.08
[152, 60] loss: 0.011
[152, 120] loss: 0.010
[152, 180] loss: 0.011
[152, 240] loss: 0.011
[152, 300] loss: 0.010
[152, 360] loss: 0.010
Epoch: 152 -> Loss: 0.0117717506364
Epoch: 152 -> Test Accuracy: 91.34
[153, 60] loss: 0.009
[153, 120] loss: 0.008
[153, 180] loss: 0.010
[153, 240] loss: 0.009
[153, 300] loss: 0.010
[153, 360] loss: 0.010
Epoch: 153 -> Loss: 0.0202627424151
Epoch: 153 -> Test Accuracy: 91.17
[154, 60] loss: 0.009
[154, 120] loss: 0.010
[154, 180] loss: 0.009
[154, 240] loss: 0.010
[154, 300] loss: 0.010
[154, 360] loss: 0.012
Epoch: 154 -> Loss: 0.00777658214793
Epoch: 154 -> Test Accuracy: 91.19
[155, 60] loss: 0.009
[155, 120] loss: 0.010
[155, 180] loss: 0.010
[155, 240] loss: 0.010
[155, 300] loss: 0.009
[155, 360] loss: 0.009
Epoch: 155 -> Loss: 0.00603697309271
Epoch: 155 -> Test Accuracy: 91.48
[156, 60] loss: 0.009
[156, 120] loss: 0.010
[156, 180] loss: 0.009
[156, 240] loss: 0.009
[156, 300] loss: 0.010
[156, 360] loss: 0.009
Epoch: 156 -> Loss: 0.0179765280336
Epoch: 156 -> Test Accuracy: 91.16
[157, 60] loss: 0.010
[157, 120] loss: 0.010
[157, 180] loss: 0.010
[157, 240] loss: 0.010
[157, 300] loss: 0.009
[157, 360] loss: 0.009
Epoch: 157 -> Loss: 0.0177674647421
Epoch: 157 -> Test Accuracy: 91.23
[158, 60] loss: 0.010
[158, 120] loss: 0.009
[158, 180] loss: 0.010
[158, 240] loss: 0.010
[158, 300] loss: 0.009
[158, 360] loss: 0.010
Epoch: 158 -> Loss: 0.00916373729706
Epoch: 158 -> Test Accuracy: 91.33
[159, 60] loss: 0.009
[159, 120] loss: 0.009
[159, 180] loss: 0.010
[159, 240] loss: 0.008
[159, 300] loss: 0.009
[159, 360] loss: 0.009
Epoch: 159 -> Loss: 0.0119180288166
Epoch: 159 -> Test Accuracy: 91.17
[160, 60] loss: 0.008
[160, 120] loss: 0.008
[160, 180] loss: 0.008
[160, 240] loss: 0.010
[160, 300] loss: 0.009
[160, 360] loss: 0.009
Epoch: 160 -> Loss: 0.0299162622541
Epoch: 160 -> Test Accuracy: 91.1
[161, 60] loss: 0.008
[161, 120] loss: 0.009
[161, 180] loss: 0.009
[161, 240] loss: 0.008
[161, 300] loss: 0.008
[161, 360] loss: 0.009
Epoch: 161 -> Loss: 0.00964940153062
Epoch: 161 -> Test Accuracy: 91.13
[162, 60] loss: 0.008
[162, 120] loss: 0.007
[162, 180] loss: 0.009
[162, 240] loss: 0.008
[162, 300] loss: 0.007
[162, 360] loss: 0.008
Epoch: 162 -> Loss: 0.00648107519373
Epoch: 162 -> Test Accuracy: 91.25
[163, 60] loss: 0.007
[163, 120] loss: 0.008
[163, 180] loss: 0.008
[163, 240] loss: 0.008
[163, 300] loss: 0.008
[163, 360] loss: 0.008
Epoch: 163 -> Loss: 0.0105269672349
Epoch: 163 -> Test Accuracy: 91.33
[164, 60] loss: 0.008
[164, 120] loss: 0.007
[164, 180] loss: 0.007
[164, 240] loss: 0.007
[164, 300] loss: 0.007
[164, 360] loss: 0.008
Epoch: 164 -> Loss: 0.0457272157073
Epoch: 164 -> Test Accuracy: 91.28
[165, 60] loss: 0.008
[165, 120] loss: 0.007
[165, 180] loss: 0.007
[165, 240] loss: 0.008
[165, 300] loss: 0.007
[165, 360] loss: 0.007
Epoch: 165 -> Loss: 0.0171728171408
Epoch: 165 -> Test Accuracy: 91.4
[166, 60] loss: 0.007
[166, 120] loss: 0.008
[166, 180] loss: 0.007
[166, 240] loss: 0.007
[166, 300] loss: 0.008
[166, 360] loss: 0.008
Epoch: 166 -> Loss: 0.0133938370273
Epoch: 166 -> Test Accuracy: 91.4
[167, 60] loss: 0.007
[167, 120] loss: 0.007
[167, 180] loss: 0.007
[167, 240] loss: 0.007
[167, 300] loss: 0.007
[167, 360] loss: 0.006
Epoch: 167 -> Loss: 0.0162021405995
Epoch: 167 -> Test Accuracy: 91.4
[168, 60] loss: 0.007
[168, 120] loss: 0.007
[168, 180] loss: 0.006
[168, 240] loss: 0.008
[168, 300] loss: 0.007
[168, 360] loss: 0.007
Epoch: 168 -> Loss: 0.00573644647375
Epoch: 168 -> Test Accuracy: 91.37
[169, 60] loss: 0.007
[169, 120] loss: 0.007
[169, 180] loss: 0.006
[169, 240] loss: 0.007
[169, 300] loss: 0.007
[169, 360] loss: 0.007
Epoch: 169 -> Loss: 0.00966047029942
Epoch: 169 -> Test Accuracy: 91.4
[170, 60] loss: 0.007
[170, 120] loss: 0.007
[170, 180] loss: 0.007
[170, 240] loss: 0.008
[170, 300] loss: 0.007
[170, 360] loss: 0.007
Epoch: 170 -> Loss: 0.0125759299845
Epoch: 170 -> Test Accuracy: 91.34
[171, 60] loss: 0.006
[171, 120] loss: 0.006
[171, 180] loss: 0.007
[171, 240] loss: 0.007
[171, 300] loss: 0.007
[171, 360] loss: 0.007
Epoch: 171 -> Loss: 0.00646898150444
Epoch: 171 -> Test Accuracy: 91.26
[172, 60] loss: 0.007
[172, 120] loss: 0.007
[172, 180] loss: 0.007
[172, 240] loss: 0.007
[172, 300] loss: 0.006
[172, 360] loss: 0.007
Epoch: 172 -> Loss: 0.0301642324775
Epoch: 172 -> Test Accuracy: 91.3
[173, 60] loss: 0.006
[173, 120] loss: 0.007
[173, 180] loss: 0.007
[173, 240] loss: 0.007
[173, 300] loss: 0.007
[173, 360] loss: 0.007
Epoch: 173 -> Loss: 0.00349472160451
Epoch: 173 -> Test Accuracy: 91.33
[174, 60] loss: 0.007
[174, 120] loss: 0.007
[174, 180] loss: 0.007
[174, 240] loss: 0.007
[174, 300] loss: 0.007
[174, 360] loss: 0.008
Epoch: 174 -> Loss: 0.00375400180928
Epoch: 174 -> Test Accuracy: 91.36
[175, 60] loss: 0.007
[175, 120] loss: 0.007
[175, 180] loss: 0.007
[175, 240] loss: 0.007
[175, 300] loss: 0.007
[175, 360] loss: 0.007
Epoch: 175 -> Loss: 0.0120144542307
Epoch: 175 -> Test Accuracy: 91.42
[176, 60] loss: 0.007
[176, 120] loss: 0.006
[176, 180] loss: 0.008
[176, 240] loss: 0.006
[176, 300] loss: 0.007
[176, 360] loss: 0.007
Epoch: 176 -> Loss: 0.0169802550226
Epoch: 176 -> Test Accuracy: 91.35
[177, 60] loss: 0.007
[177, 120] loss: 0.007
[177, 180] loss: 0.007
[177, 240] loss: 0.007
[177, 300] loss: 0.007
[177, 360] loss: 0.006
Epoch: 177 -> Loss: 0.00630267243832
Epoch: 177 -> Test Accuracy: 91.38
[178, 60] loss: 0.007
[178, 120] loss: 0.007
[178, 180] loss: 0.007
[178, 240] loss: 0.007
[178, 300] loss: 0.006
[178, 360] loss: 0.007
Epoch: 178 -> Loss: 0.0068407417275
Epoch: 178 -> Test Accuracy: 91.21
[179, 60] loss: 0.007
[179, 120] loss: 0.007
[179, 180] loss: 0.007
[179, 240] loss: 0.007
[179, 300] loss: 0.007
[179, 360] loss: 0.007
Epoch: 179 -> Loss: 0.01610468328
Epoch: 179 -> Test Accuracy: 91.35
[180, 60] loss: 0.007
[180, 120] loss: 0.006
[180, 180] loss: 0.007
[180, 240] loss: 0.006
[180, 300] loss: 0.007
[180, 360] loss: 0.008
Epoch: 180 -> Loss: 0.00931457895786
Epoch: 180 -> Test Accuracy: 91.29
[181, 60] loss: 0.007
[181, 120] loss: 0.007
[181, 180] loss: 0.007
[181, 240] loss: 0.007
[181, 300] loss: 0.007
[181, 360] loss: 0.007
Epoch: 181 -> Loss: 0.00952905416489
Epoch: 181 -> Test Accuracy: 91.34
[182, 60] loss: 0.006
[182, 120] loss: 0.007
[182, 180] loss: 0.007
[182, 240] loss: 0.006
[182, 300] loss: 0.008
[182, 360] loss: 0.006
Epoch: 182 -> Loss: 0.00699643511325
Epoch: 182 -> Test Accuracy: 91.43
[183, 60] loss: 0.007
[183, 120] loss: 0.007
[183, 180] loss: 0.007
[183, 240] loss: 0.008
[183, 300] loss: 0.006
[183, 360] loss: 0.006
Epoch: 183 -> Loss: 0.00621772417799
Epoch: 183 -> Test Accuracy: 91.32
[184, 60] loss: 0.007
[184, 120] loss: 0.007
[184, 180] loss: 0.006
[184, 240] loss: 0.007
[184, 300] loss: 0.007
[184, 360] loss: 0.007
Epoch: 184 -> Loss: 0.00833785533905
Epoch: 184 -> Test Accuracy: 91.33
[185, 60] loss: 0.007
[185, 120] loss: 0.007
[185, 180] loss: 0.008
[185, 240] loss: 0.007
[185, 300] loss: 0.006
[185, 360] loss: 0.006
Epoch: 185 -> Loss: 0.00589191913605
Epoch: 185 -> Test Accuracy: 91.3
[186, 60] loss: 0.007
[186, 120] loss: 0.008
[186, 180] loss: 0.007
[186, 240] loss: 0.007
[186, 300] loss: 0.007
[186, 360] loss: 0.008
Epoch: 186 -> Loss: 0.00488076219335
Epoch: 186 -> Test Accuracy: 91.36
[187, 60] loss: 0.006
[187, 120] loss: 0.007
[187, 180] loss: 0.006
[187, 240] loss: 0.007
[187, 300] loss: 0.007
[187, 360] loss: 0.007
Epoch: 187 -> Loss: 0.0116668641567
Epoch: 187 -> Test Accuracy: 91.44
[188, 60] loss: 0.007
[188, 120] loss: 0.007
[188, 180] loss: 0.007
[188, 240] loss: 0.007
[188, 300] loss: 0.007
[188, 360] loss: 0.006
Epoch: 188 -> Loss: 0.00331798801199
Epoch: 188 -> Test Accuracy: 91.47
[189, 60] loss: 0.006
[189, 120] loss: 0.006
[189, 180] loss: 0.007
[189, 240] loss: 0.007
[189, 300] loss: 0.007
[189, 360] loss: 0.007
Epoch: 189 -> Loss: 0.00637602806091
Epoch: 189 -> Test Accuracy: 91.4
[190, 60] loss: 0.007
[190, 120] loss: 0.007
[190, 180] loss: 0.007
[190, 240] loss: 0.007
[190, 300] loss: 0.006
[190, 360] loss: 0.007
Epoch: 190 -> Loss: 0.0175461675972
Epoch: 190 -> Test Accuracy: 91.38
[191, 60] loss: 0.007
[191, 120] loss: 0.007
[191, 180] loss: 0.007
[191, 240] loss: 0.007
[191, 300] loss: 0.006
[191, 360] loss: 0.006
Epoch: 191 -> Loss: 0.00864890217781
Epoch: 191 -> Test Accuracy: 91.42
[192, 60] loss: 0.007
[192, 120] loss: 0.007
[192, 180] loss: 0.006
[192, 240] loss: 0.006
[192, 300] loss: 0.007
[192, 360] loss: 0.006
Epoch: 192 -> Loss: 0.003874379443
Epoch: 192 -> Test Accuracy: 91.48
[193, 60] loss: 0.007
[193, 120] loss: 0.006
[193, 180] loss: 0.006
[193, 240] loss: 0.007
[193, 300] loss: 0.007
[193, 360] loss: 0.008
Epoch: 193 -> Loss: 0.00823497213423
Epoch: 193 -> Test Accuracy: 91.44
[194, 60] loss: 0.007
[194, 120] loss: 0.006
[194, 180] loss: 0.006
[194, 240] loss: 0.007
[194, 300] loss: 0.007
[194, 360] loss: 0.007
Epoch: 194 -> Loss: 0.0043452619575
Epoch: 194 -> Test Accuracy: 91.34
[195, 60] loss: 0.006
[195, 120] loss: 0.006
[195, 180] loss: 0.007
[195, 240] loss: 0.007
[195, 300] loss: 0.006
[195, 360] loss: 0.007
Epoch: 195 -> Loss: 0.0079355482012
Epoch: 195 -> Test Accuracy: 91.53
[196, 60] loss: 0.006
[196, 120] loss: 0.006
[196, 180] loss: 0.006
[196, 240] loss: 0.006
[196, 300] loss: 0.007
[196, 360] loss: 0.006
Epoch: 196 -> Loss: 0.0104473233223
Epoch: 196 -> Test Accuracy: 91.46
[197, 60] loss: 0.007
[197, 120] loss: 0.006
[197, 180] loss: 0.007
[197, 240] loss: 0.007
[197, 300] loss: 0.007
[197, 360] loss: 0.007
Epoch: 197 -> Loss: 0.00406329613179
Epoch: 197 -> Test Accuracy: 91.51
[198, 60] loss: 0.007
[198, 120] loss: 0.007
[198, 180] loss: 0.006
[198, 240] loss: 0.007
[198, 300] loss: 0.007
[198, 360] loss: 0.007
Epoch: 198 -> Loss: 0.0194802395999
Epoch: 198 -> Test Accuracy: 91.48
[199, 60] loss: 0.007
[199, 120] loss: 0.006
[199, 180] loss: 0.007
[199, 240] loss: 0.006
[199, 300] loss: 0.007
[199, 360] loss: 0.007
Epoch: 199 -> Loss: 0.00719060888514
Epoch: 199 -> Test Accuracy: 91.49
[200, 60] loss: 0.007
[200, 120] loss: 0.006
[200, 180] loss: 0.006
[200, 240] loss: 0.007
[200, 300] loss: 0.007
[200, 360] loss: 0.006
Epoch: 200 -> Loss: 0.00321622495539
Epoch: 200 -> Test Accuracy: 91.37
Finished Training
In [14]:
# save variables
fm.save_variable([semi_loss_log, semi_accuracy_log, super_loss_log, super_accuracy_log], "semi-supervised")

Evaluate Test Accuracies

In [6]:
# 3 ConvBlock RotNet model and Classifiers
ev.evaluate_all(3, testloader, classes)
Evaluating RotNet model with 3 Convolutional Blocks:


Evaluating Rotation Task:
Test Accuracy:  92.190 %
Accuracy per class:
Test Accuracy of original :  92.100 %
Test Accuracy of 90 rotation :  92.110 %
Test Accuracy of 180 rotation :  92.140 %
Test Accuracy of 270 rotation :  92.410 %


--------------------------------------------------------------------------------


Starting to evaluate Non-Linear Classifier:


Evaluating Non-Linear Classifier on Convolutional Block 1:
Test Accuracy:  83.280 %
Accuracy per class:
Test Accuracy of plane :  85.100 %
Test Accuracy of car :  91.400 %
Test Accuracy of bird :  76.900 %
Test Accuracy of cat :  69.000 %
Test Accuracy of deer :  79.700 %
Test Accuracy of dog :  72.900 %
Test Accuracy of frog :  89.500 %
Test Accuracy of horse :  86.300 %
Test Accuracy of ship :  91.100 %
Test Accuracy of truck :  90.900 %


Evaluating Non-Linear Classifier on Convolutional Block 2:
Test Accuracy:  86.510 %
Accuracy per class:
Test Accuracy of plane :  87.300 %
Test Accuracy of car :  92.600 %
Test Accuracy of bird :  82.400 %
Test Accuracy of cat :  77.100 %
Test Accuracy of deer :  86.600 %
Test Accuracy of dog :  77.600 %
Test Accuracy of frog :  90.400 %
Test Accuracy of horse :  88.600 %
Test Accuracy of ship :  92.400 %
Test Accuracy of truck :  90.100 %


Evaluating Non-Linear Classifier on Convolutional Block 3:
Test Accuracy:  54.070 %
Accuracy per class:
Test Accuracy of plane :  61.800 %
Test Accuracy of car :  59.200 %
Test Accuracy of bird :  43.100 %
Test Accuracy of cat :  36.500 %
Test Accuracy of deer :  50.300 %
Test Accuracy of dog :  51.100 %
Test Accuracy of frog :  61.300 %
Test Accuracy of horse :  54.600 %
Test Accuracy of ship :  60.000 %
Test Accuracy of truck :  62.800 %


--------------------------------------------------------------------------------


Starting to evaluate Convolutional Classifier:


Evaluating Convolutional Classifier on Convolutional Block 1
Test Accuracy:  86.850 %
Accuracy per class:
Test Accuracy of plane :  88.300 %
Test Accuracy of car :  93.900 %
Test Accuracy of bird :  80.200 %
Test Accuracy of cat :  76.900 %
Test Accuracy of deer :  86.700 %
Test Accuracy of dog :  78.400 %
Test Accuracy of frog :  92.500 %
Test Accuracy of horse :  89.200 %
Test Accuracy of ship :  91.500 %
Test Accuracy of truck :  90.900 %


Evaluating Convolutional Classifier on Convolutional Block 2
Test Accuracy:  88.820 %
Accuracy per class:
Test Accuracy of plane :  89.200 %
Test Accuracy of car :  94.000 %
Test Accuracy of bird :  84.400 %
Test Accuracy of cat :  79.900 %
Test Accuracy of deer :  89.800 %
Test Accuracy of dog :  83.400 %
Test Accuracy of frog :  92.200 %
Test Accuracy of horse :  90.000 %
Test Accuracy of ship :  93.300 %
Test Accuracy of truck :  92.000 %


Evaluating Convolutional Classifier on Convolutional Block 3
Test Accuracy:  61.910 %
Accuracy per class:
Test Accuracy of plane :  64.700 %
Test Accuracy of car :  64.500 %
Test Accuracy of bird :  51.100 %
Test Accuracy of cat :  49.300 %
Test Accuracy of deer :  61.200 %
Test Accuracy of dog :  55.300 %
Test Accuracy of frog :  71.600 %
Test Accuracy of horse :  61.400 %
Test Accuracy of ship :  67.900 %
Test Accuracy of truck :  72.100 %
Out[6]:
{'Accuracy ConvClassifier ConvBlock 1': 86.85,
 'Accuracy ConvClassifier ConvBlock 2': 88.82,
 'Accuracy ConvClassifier ConvBlock 3': 61.91,
 'Accuracy Non-Linear ConvBlock 1': 83.28,
 'Accuracy Non-Linear ConvBlock 2': 86.51,
 'Accuracy Non-Linear ConvBlock 3': 54.07,
 'Accuracy Rotation Task': 92.19,
 'Class Accuracy ConvClassifier ConvBlock 1': [88.3,
  93.9,
  80.2,
  76.9,
  86.7,
  78.4,
  92.5,
  89.2,
  91.5,
  90.9],
 'Class Accuracy ConvClassifier ConvBlock 2': [89.2,
  94.0,
  84.4,
  79.9,
  89.8,
  83.4,
  92.2,
  90.0,
  93.3,
  92.0],
 'Class Accuracy ConvClassifier ConvBlock 3': [64.7,
  64.5,
  51.1,
  49.3,
  61.2,
  55.3,
  71.6,
  61.4,
  67.9,
  72.1],
 'Class Accuracy Non-Linear ConvBlock 1': [85.1,
  91.4,
  76.9,
  69.0,
  79.7,
  72.9,
  89.5,
  86.3,
  91.1,
  90.9],
 'Class Accuracy Non-Linear ConvBlock 2': [87.3,
  92.6,
  82.4,
  77.1,
  86.6,
  77.6,
  90.4,
  88.6,
  92.4,
  90.1],
 'Class Accuracy Non-Linear ConvBlock 3': [61.8,
  59.2,
  43.1,
  36.5,
  50.3,
  51.1,
  61.3,
  54.6,
  60.0,
  62.8],
 'Class Accuracy Rotation Task': [92.1, 92.11, 92.14, 92.41]}
In [15]:
# 4 ConvBlock RotNet model and Classifiers
ev.evaluate_all(4, testloader, classes)
Evaluating RotNet model with 4 Convolutional Blocks:


Evaluating Rotation Task:
Test Accuracy:  92.632 %
Accuracy per class:
Test Accuracy of original :  92.560 %
Test Accuracy of 90 rotation :  92.350 %
Test Accuracy of 180 rotation :  92.880 %
Test Accuracy of 270 rotation :  92.740 %


--------------------------------------------------------------------------------


Starting to evaluate Non-Linear Classifier:


Evaluating Non-Linear Classifier on Convolutional Block 1:
Test Accuracy:  83.120 %
Accuracy per class:
Test Accuracy of plane :  84.200 %
Test Accuracy of car :  91.800 %
Test Accuracy of bird :  75.800 %
Test Accuracy of cat :  67.300 %
Test Accuracy of deer :  82.400 %
Test Accuracy of dog :  74.100 %
Test Accuracy of frog :  88.700 %
Test Accuracy of horse :  86.600 %
Test Accuracy of ship :  90.800 %
Test Accuracy of truck :  89.500 %


Evaluating Non-Linear Classifier on Convolutional Block 2:
Test Accuracy:  86.600 %
Accuracy per class:
Test Accuracy of plane :  86.700 %
Test Accuracy of car :  92.500 %
Test Accuracy of bird :  82.700 %
Test Accuracy of cat :  75.300 %
Test Accuracy of deer :  85.300 %
Test Accuracy of dog :  80.000 %
Test Accuracy of frog :  91.300 %
Test Accuracy of horse :  89.500 %
Test Accuracy of ship :  91.200 %
Test Accuracy of truck :  91.500 %


Evaluating Non-Linear Classifier on Convolutional Block 3:
Test Accuracy:  82.540 %
Accuracy per class:
Test Accuracy of plane :  82.400 %
Test Accuracy of car :  90.000 %
Test Accuracy of bird :  78.000 %
Test Accuracy of cat :  72.000 %
Test Accuracy of deer :  81.400 %
Test Accuracy of dog :  74.300 %
Test Accuracy of frog :  87.500 %
Test Accuracy of horse :  86.600 %
Test Accuracy of ship :  86.500 %
Test Accuracy of truck :  86.700 %


Evaluating Non-Linear Classifier on Convolutional Block 4:
Test Accuracy:  45.300 %
Accuracy per class:
Test Accuracy of plane :  55.900 %
Test Accuracy of car :  49.300 %
Test Accuracy of bird :  34.200 %
Test Accuracy of cat :  34.800 %
Test Accuracy of deer :  34.200 %
Test Accuracy of dog :  38.600 %
Test Accuracy of frog :  56.200 %
Test Accuracy of horse :  47.900 %
Test Accuracy of ship :  51.800 %
Test Accuracy of truck :  50.100 %


--------------------------------------------------------------------------------


Starting to evaluate Convolutional Classifier:


Evaluating Convolutional Classifier on Convolutional Block 1
Test Accuracy:  86.580 %
Accuracy per class:
Test Accuracy of plane :  89.300 %
Test Accuracy of car :  93.700 %
Test Accuracy of bird :  80.000 %
Test Accuracy of cat :  75.200 %
Test Accuracy of deer :  84.200 %
Test Accuracy of dog :  79.200 %
Test Accuracy of frog :  91.700 %
Test Accuracy of horse :  88.700 %
Test Accuracy of ship :  91.800 %
Test Accuracy of truck :  92.000 %


Evaluating Convolutional Classifier on Convolutional Block 2
Test Accuracy:  88.830 %
Accuracy per class:
Test Accuracy of plane :  88.400 %
Test Accuracy of car :  93.100 %
Test Accuracy of bird :  86.100 %
Test Accuracy of cat :  78.900 %
Test Accuracy of deer :  89.300 %
Test Accuracy of dog :  84.500 %
Test Accuracy of frog :  92.000 %
Test Accuracy of horse :  91.300 %
Test Accuracy of ship :  93.200 %
Test Accuracy of truck :  91.500 %


Evaluating Convolutional Classifier on Convolutional Block 3
Test Accuracy:  84.250 %
Accuracy per class:
Test Accuracy of plane :  85.000 %
Test Accuracy of car :  90.400 %
Test Accuracy of bird :  79.700 %
Test Accuracy of cat :  68.800 %
Test Accuracy of deer :  84.200 %
Test Accuracy of dog :  80.100 %
Test Accuracy of frog :  89.100 %
Test Accuracy of horse :  87.800 %
Test Accuracy of ship :  89.300 %
Test Accuracy of truck :  88.100 %


Evaluating Convolutional Classifier on Convolutional Block 4
Test Accuracy:  53.400 %
Accuracy per class:
Test Accuracy of plane :  62.600 %
Test Accuracy of car :  57.600 %
Test Accuracy of bird :  41.900 %
Test Accuracy of cat :  35.100 %
Test Accuracy of deer :  48.600 %
Test Accuracy of dog :  49.300 %
Test Accuracy of frog :  64.600 %
Test Accuracy of horse :  55.400 %
Test Accuracy of ship :  60.200 %
Test Accuracy of truck :  58.700 %
Out[15]:
{'Accuracy ConvClassifier ConvBlock 1': 86.58,
 'Accuracy ConvClassifier ConvBlock 2': 88.83,
 'Accuracy ConvClassifier ConvBlock 3': 84.25,
 'Accuracy ConvClassifier ConvBlock 4': 53.4,
 'Accuracy Non-Linear ConvBlock 1': 83.12,
 'Accuracy Non-Linear ConvBlock 2': 86.6,
 'Accuracy Non-Linear ConvBlock 3': 82.54,
 'Accuracy Non-Linear ConvBlock 4': 45.3,
 'Accuracy Rotation Task': 92.6325,
 'Class Accuracy ConvClassifier ConvBlock 1': [89.3,
  93.7,
  80.0,
  75.2,
  84.2,
  79.2,
  91.7,
  88.7,
  91.8,
  92.0],
 'Class Accuracy ConvClassifier ConvBlock 2': [88.4,
  93.1,
  86.1,
  78.9,
  89.3,
  84.5,
  92.0,
  91.3,
  93.2,
  91.5],
 'Class Accuracy ConvClassifier ConvBlock 3': [85.0,
  90.4,
  79.7,
  68.8,
  84.2,
  80.1,
  89.1,
  87.8,
  89.3,
  88.1],
 'Class Accuracy ConvClassifier ConvBlock 4': [62.6,
  57.6,
  41.9,
  35.1,
  48.6,
  49.3,
  64.6,
  55.4,
  60.2,
  58.7],
 'Class Accuracy Non-Linear ConvBlock 1': [84.2,
  91.8,
  75.8,
  67.3,
  82.4,
  74.1,
  88.7,
  86.6,
  90.8,
  89.5],
 'Class Accuracy Non-Linear ConvBlock 2': [86.7,
  92.5,
  82.7,
  75.3,
  85.3,
  80.0,
  91.3,
  89.5,
  91.2,
  91.5],
 'Class Accuracy Non-Linear ConvBlock 3': [82.4,
  90.0,
  78.0,
  72.0,
  81.4,
  74.3,
  87.5,
  86.6,
  86.5,
  86.7],
 'Class Accuracy Non-Linear ConvBlock 4': [55.9,
  49.3,
  34.2,
  34.8,
  34.2,
  38.6,
  56.2,
  47.9,
  51.8,
  50.1],
 'Class Accuracy Rotation Task': [92.56, 92.35, 92.88, 92.74]}
In [8]:
# 5 ConvBlock RotNet model and Classifiers
ev.evaluate_all(5, testloader, classes)
Evaluating RotNet model with 5 Convolutional Blocks:


Evaluating Rotation Task:
Test Accuracy:  92.088 %
Accuracy per class:
Test Accuracy of original :  92.380 %
Test Accuracy of 90 rotation :  92.150 %
Test Accuracy of 180 rotation :  91.850 %
Test Accuracy of 270 rotation :  91.970 %


--------------------------------------------------------------------------------


Starting to evaluate Non-Linear Classifier:


Evaluating Non-Linear Classifier on Convolutional Block 1:
Test Accuracy:  82.990 %
Accuracy per class:
Test Accuracy of plane :  82.700 %
Test Accuracy of car :  90.200 %
Test Accuracy of bird :  77.800 %
Test Accuracy of cat :  68.300 %
Test Accuracy of deer :  79.100 %
Test Accuracy of dog :  73.500 %
Test Accuracy of frog :  88.600 %
Test Accuracy of horse :  88.000 %
Test Accuracy of ship :  91.800 %
Test Accuracy of truck :  89.900 %


Evaluating Non-Linear Classifier on Convolutional Block 2:
Test Accuracy:  86.610 %
Accuracy per class:
Test Accuracy of plane :  88.900 %
Test Accuracy of car :  92.700 %
Test Accuracy of bird :  82.300 %
Test Accuracy of cat :  74.200 %
Test Accuracy of deer :  84.800 %
Test Accuracy of dog :  79.800 %
Test Accuracy of frog :  90.700 %
Test Accuracy of horse :  88.600 %
Test Accuracy of ship :  92.000 %
Test Accuracy of truck :  92.100 %


Evaluating Non-Linear Classifier on Convolutional Block 3:
Test Accuracy:  82.970 %
Accuracy per class:
Test Accuracy of plane :  83.700 %
Test Accuracy of car :  90.000 %
Test Accuracy of bird :  77.300 %
Test Accuracy of cat :  73.800 %
Test Accuracy of deer :  80.500 %
Test Accuracy of dog :  73.900 %
Test Accuracy of frog :  87.700 %
Test Accuracy of horse :  85.300 %
Test Accuracy of ship :  90.300 %
Test Accuracy of truck :  87.200 %


Evaluating Non-Linear Classifier on Convolutional Block 4:
Test Accuracy:  69.830 %
Accuracy per class:
Test Accuracy of plane :  78.000 %
Test Accuracy of car :  73.600 %
Test Accuracy of bird :  60.800 %
Test Accuracy of cat :  54.600 %
Test Accuracy of deer :  67.200 %
Test Accuracy of dog :  59.500 %
Test Accuracy of frog :  79.400 %
Test Accuracy of horse :  73.900 %
Test Accuracy of ship :  75.700 %
Test Accuracy of truck :  75.600 %


Evaluating Non-Linear Classifier on Convolutional Block 5:
Test Accuracy:  36.900 %
Accuracy per class:
Test Accuracy of plane :  44.700 %
Test Accuracy of car :  39.400 %
Test Accuracy of bird :  29.400 %
Test Accuracy of cat :  26.200 %
Test Accuracy of deer :  24.300 %
Test Accuracy of dog :  31.500 %
Test Accuracy of frog :  42.900 %
Test Accuracy of horse :  40.200 %
Test Accuracy of ship :  47.900 %
Test Accuracy of truck :  42.500 %


--------------------------------------------------------------------------------


Starting to evaluate Convolutional Classifier:


Evaluating Convolutional Classifier on Convolutional Block 1
Test Accuracy:  86.150 %
Accuracy per class:
Test Accuracy of plane :  89.500 %
Test Accuracy of car :  93.000 %
Test Accuracy of bird :  79.100 %
Test Accuracy of cat :  73.000 %
Test Accuracy of deer :  86.400 %
Test Accuracy of dog :  79.900 %
Test Accuracy of frog :  90.700 %
Test Accuracy of horse :  88.300 %
Test Accuracy of ship :  90.700 %
Test Accuracy of truck :  90.900 %


Evaluating Convolutional Classifier on Convolutional Block 2
Test Accuracy:  88.370 %
Accuracy per class:
Test Accuracy of plane :  88.900 %
Test Accuracy of car :  92.700 %
Test Accuracy of bird :  84.400 %
Test Accuracy of cat :  79.700 %
Test Accuracy of deer :  87.900 %
Test Accuracy of dog :  81.500 %
Test Accuracy of frog :  91.500 %
Test Accuracy of horse :  88.900 %
Test Accuracy of ship :  94.300 %
Test Accuracy of truck :  93.900 %


Evaluating Convolutional Classifier on Convolutional Block 3
Test Accuracy:  85.150 %
Accuracy per class:
Test Accuracy of plane :  86.800 %
Test Accuracy of car :  90.700 %
Test Accuracy of bird :  79.000 %
Test Accuracy of cat :  76.600 %
Test Accuracy of deer :  84.900 %
Test Accuracy of dog :  76.100 %
Test Accuracy of frog :  89.800 %
Test Accuracy of horse :  87.400 %
Test Accuracy of ship :  90.500 %
Test Accuracy of truck :  89.700 %


Evaluating Convolutional Classifier on Convolutional Block 4
Test Accuracy:  72.950 %
Accuracy per class:
Test Accuracy of plane :  78.400 %
Test Accuracy of car :  77.300 %
Test Accuracy of bird :  63.700 %
Test Accuracy of cat :  61.500 %
Test Accuracy of deer :  72.900 %
Test Accuracy of dog :  65.000 %
Test Accuracy of frog :  81.600 %
Test Accuracy of horse :  75.700 %
Test Accuracy of ship :  76.100 %
Test Accuracy of truck :  77.300 %


Evaluating Convolutional Classifier on Convolutional Block 5
Test Accuracy:  41.480 %
Accuracy per class:
Test Accuracy of plane :  49.200 %
Test Accuracy of car :  44.200 %
Test Accuracy of bird :  33.700 %
Test Accuracy of cat :  33.900 %
Test Accuracy of deer :  27.800 %
Test Accuracy of dog :  35.900 %
Test Accuracy of frog :  52.300 %
Test Accuracy of horse :  41.400 %
Test Accuracy of ship :  50.000 %
Test Accuracy of truck :  46.400 %
Out[8]:
{'Accuracy ConvClassifier ConvBlock 1': 86.15,
 'Accuracy ConvClassifier ConvBlock 2': 88.37,
 'Accuracy ConvClassifier ConvBlock 3': 85.15,
 'Accuracy ConvClassifier ConvBlock 4': 72.95,
 'Accuracy ConvClassifier ConvBlock 5': 41.48,
 'Accuracy Non-Linear ConvBlock 1': 82.99,
 'Accuracy Non-Linear ConvBlock 2': 86.61,
 'Accuracy Non-Linear ConvBlock 3': 82.97,
 'Accuracy Non-Linear ConvBlock 4': 69.83,
 'Accuracy Non-Linear ConvBlock 5': 36.9,
 'Accuracy Rotation Task': 92.0875,
 'Class Accuracy ConvClassifier ConvBlock 1': [89.5,
  93.0,
  79.1,
  73.0,
  86.4,
  79.9,
  90.7,
  88.3,
  90.7,
  90.9],
 'Class Accuracy ConvClassifier ConvBlock 2': [88.9,
  92.7,
  84.4,
  79.7,
  87.9,
  81.5,
  91.5,
  88.9,
  94.3,
  93.9],
 'Class Accuracy ConvClassifier ConvBlock 3': [86.8,
  90.7,
  79.0,
  76.6,
  84.9,
  76.1,
  89.8,
  87.4,
  90.5,
  89.7],
 'Class Accuracy ConvClassifier ConvBlock 4': [78.4,
  77.3,
  63.7,
  61.5,
  72.9,
  65.0,
  81.6,
  75.7,
  76.1,
  77.3],
 'Class Accuracy ConvClassifier ConvBlock 5': [49.2,
  44.2,
  33.7,
  33.9,
  27.8,
  35.9,
  52.3,
  41.4,
  50.0,
  46.4],
 'Class Accuracy Non-Linear ConvBlock 1': [82.7,
  90.2,
  77.8,
  68.3,
  79.1,
  73.5,
  88.6,
  88.0,
  91.8,
  89.9],
 'Class Accuracy Non-Linear ConvBlock 2': [88.9,
  92.7,
  82.3,
  74.2,
  84.8,
  79.8,
  90.7,
  88.6,
  92.0,
  92.1],
 'Class Accuracy Non-Linear ConvBlock 3': [83.7,
  90.0,
  77.3,
  73.8,
  80.5,
  73.9,
  87.7,
  85.3,
  90.3,
  87.2],
 'Class Accuracy Non-Linear ConvBlock 4': [78.0,
  73.6,
  60.8,
  54.6,
  67.2,
  59.5,
  79.4,
  73.9,
  75.7,
  75.6],
 'Class Accuracy Non-Linear ConvBlock 5': [44.7,
  39.4,
  29.4,
  26.2,
  24.3,
  31.5,
  42.9,
  40.2,
  47.9,
  42.5],
 'Class Accuracy Rotation Task': [92.38, 92.15, 91.85, 91.97]}
In [13]:
# 5 ConvBlock RotNet model and Classifiers new
ev.evaluate_all(5, testloader, classes)
Evaluating RotNet model with 5 Convolutional Blocks:


Evaluating Rotation Task:
Test Accuracy:  92.225 %
Accuracy per class:
Test Accuracy of original :  92.460 %
Test Accuracy of 90 rotation :  92.530 %
Test Accuracy of 180 rotation :  92.180 %
Test Accuracy of 270 rotation :  91.730 %


--------------------------------------------------------------------------------


Starting to evaluate Non-Linear Classifier:


Evaluating Non-Linear Classifier on Convolutional Block 1:
Test Accuracy:  82.640 %
Accuracy per class:
Test Accuracy of plane :  84.200 %
Test Accuracy of car :  90.300 %
Test Accuracy of bird :  76.000 %
Test Accuracy of cat :  67.700 %
Test Accuracy of deer :  81.900 %
Test Accuracy of dog :  73.100 %
Test Accuracy of frog :  88.000 %
Test Accuracy of horse :  86.400 %
Test Accuracy of ship :  90.900 %
Test Accuracy of truck :  87.900 %


Evaluating Non-Linear Classifier on Convolutional Block 2:
Test Accuracy:  86.980 %
Accuracy per class:
Test Accuracy of plane :  88.900 %
Test Accuracy of car :  92.400 %
Test Accuracy of bird :  83.400 %
Test Accuracy of cat :  75.600 %
Test Accuracy of deer :  86.200 %
Test Accuracy of dog :  80.600 %
Test Accuracy of frog :  91.200 %
Test Accuracy of horse :  89.300 %
Test Accuracy of ship :  92.000 %
Test Accuracy of truck :  90.200 %


Evaluating Non-Linear Classifier on Convolutional Block 3:
Test Accuracy:  83.740 %
Accuracy per class:
Test Accuracy of plane :  85.100 %
Test Accuracy of car :  90.500 %
Test Accuracy of bird :  79.400 %
Test Accuracy of cat :  71.700 %
Test Accuracy of deer :  81.900 %
Test Accuracy of dog :  75.500 %
Test Accuracy of frog :  88.700 %
Test Accuracy of horse :  86.200 %
Test Accuracy of ship :  90.100 %
Test Accuracy of truck :  88.300 %


Evaluating Non-Linear Classifier on Convolutional Block 4:
Test Accuracy:  75.130 %
Accuracy per class:
Test Accuracy of plane :  78.000 %
Test Accuracy of car :  79.000 %
Test Accuracy of bird :  67.600 %
Test Accuracy of cat :  63.000 %
Test Accuracy of deer :  73.900 %
Test Accuracy of dog :  62.900 %
Test Accuracy of frog :  85.800 %
Test Accuracy of horse :  80.300 %
Test Accuracy of ship :  80.900 %
Test Accuracy of truck :  79.900 %


Evaluating Non-Linear Classifier on Convolutional Block 5:
Test Accuracy:  39.120 %
Accuracy per class:
Test Accuracy of plane :  45.100 %
Test Accuracy of car :  42.400 %
Test Accuracy of bird :  29.900 %
Test Accuracy of cat :  27.400 %
Test Accuracy of deer :  30.900 %
Test Accuracy of dog :  33.600 %
Test Accuracy of frog :  48.600 %
Test Accuracy of horse :  40.700 %
Test Accuracy of ship :  48.600 %
Test Accuracy of truck :  44.000 %


--------------------------------------------------------------------------------


Starting to evaluate Convolutional Classifier:


Evaluating Convolutional Classifier on Convolutional Block 1
Test Accuracy:  85.280 %
Accuracy per class:
Test Accuracy of plane :  87.800 %
Test Accuracy of car :  92.700 %
Test Accuracy of bird :  79.600 %
Test Accuracy of cat :  73.300 %
Test Accuracy of deer :  81.900 %
Test Accuracy of dog :  77.100 %
Test Accuracy of frog :  91.200 %
Test Accuracy of horse :  88.100 %
Test Accuracy of ship :  91.400 %
Test Accuracy of truck :  89.700 %


Evaluating Convolutional Classifier on Convolutional Block 2
Test Accuracy:  89.000 %
Accuracy per class:
Test Accuracy of plane :  90.900 %
Test Accuracy of car :  92.700 %
Test Accuracy of bird :  85.000 %
Test Accuracy of cat :  80.900 %
Test Accuracy of deer :  89.600 %
Test Accuracy of dog :  82.000 %
Test Accuracy of frog :  93.100 %
Test Accuracy of horse :  92.400 %
Test Accuracy of ship :  91.600 %
Test Accuracy of truck :  91.800 %


Evaluating Convolutional Classifier on Convolutional Block 3
Test Accuracy:  85.400 %
Accuracy per class:
Test Accuracy of plane :  85.800 %
Test Accuracy of car :  89.800 %
Test Accuracy of bird :  80.300 %
Test Accuracy of cat :  76.000 %
Test Accuracy of deer :  87.700 %
Test Accuracy of dog :  76.800 %
Test Accuracy of frog :  90.500 %
Test Accuracy of horse :  88.000 %
Test Accuracy of ship :  90.700 %
Test Accuracy of truck :  88.400 %


Evaluating Convolutional Classifier on Convolutional Block 4
Test Accuracy:  76.960 %
Accuracy per class:
Test Accuracy of plane :  78.200 %
Test Accuracy of car :  78.500 %
Test Accuracy of bird :  72.200 %
Test Accuracy of cat :  63.700 %
Test Accuracy of deer :  78.400 %
Test Accuracy of dog :  68.800 %
Test Accuracy of frog :  85.700 %
Test Accuracy of horse :  78.700 %
Test Accuracy of ship :  83.500 %
Test Accuracy of truck :  81.900 %


Evaluating Convolutional Classifier on Convolutional Block 5
Test Accuracy:  43.900 %
Accuracy per class:
Test Accuracy of plane :  56.000 %
Test Accuracy of car :  44.100 %
Test Accuracy of bird :  38.400 %
Test Accuracy of cat :  32.800 %
Test Accuracy of deer :  33.400 %
Test Accuracy of dog :  36.300 %
Test Accuracy of frog :  51.400 %
Test Accuracy of horse :  46.400 %
Test Accuracy of ship :  48.100 %
Test Accuracy of truck :  52.100 %
Out[13]:
{'Accuracy ConvClassifier ConvBlock 1': 85.28,
 'Accuracy ConvClassifier ConvBlock 2': 89.0,
 'Accuracy ConvClassifier ConvBlock 3': 85.4,
 'Accuracy ConvClassifier ConvBlock 4': 76.96,
 'Accuracy ConvClassifier ConvBlock 5': 43.9,
 'Accuracy Non-Linear ConvBlock 1': 82.64,
 'Accuracy Non-Linear ConvBlock 2': 86.98,
 'Accuracy Non-Linear ConvBlock 3': 83.74,
 'Accuracy Non-Linear ConvBlock 4': 75.13,
 'Accuracy Non-Linear ConvBlock 5': 39.12,
 'Accuracy Rotation Task': 92.225,
 'Class Accuracy ConvClassifier ConvBlock 1': [87.8,
  92.7,
  79.6,
  73.3,
  81.9,
  77.1,
  91.2,
  88.1,
  91.4,
  89.7],
 'Class Accuracy ConvClassifier ConvBlock 2': [90.9,
  92.7,
  85.0,
  80.9,
  89.6,
  82.0,
  93.1,
  92.4,
  91.6,
  91.8],
 'Class Accuracy ConvClassifier ConvBlock 3': [85.8,
  89.8,
  80.3,
  76.0,
  87.7,
  76.8,
  90.5,
  88.0,
  90.7,
  88.4],
 'Class Accuracy ConvClassifier ConvBlock 4': [78.2,
  78.5,
  72.2,
  63.7,
  78.4,
  68.8,
  85.7,
  78.7,
  83.5,
  81.9],
 'Class Accuracy ConvClassifier ConvBlock 5': [56.0,
  44.1,
  38.4,
  32.8,
  33.4,
  36.3,
  51.4,
  46.4,
  48.1,
  52.1],
 'Class Accuracy Non-Linear ConvBlock 1': [84.2,
  90.3,
  76.0,
  67.7,
  81.9,
  73.1,
  88.0,
  86.4,
  90.9,
  87.9],
 'Class Accuracy Non-Linear ConvBlock 2': [88.9,
  92.4,
  83.4,
  75.6,
  86.2,
  80.6,
  91.2,
  89.3,
  92.0,
  90.2],
 'Class Accuracy Non-Linear ConvBlock 3': [85.1,
  90.5,
  79.4,
  71.7,
  81.9,
  75.5,
  88.7,
  86.2,
  90.1,
  88.3],
 'Class Accuracy Non-Linear ConvBlock 4': [78.0,
  79.0,
  67.6,
  63.0,
  73.9,
  62.9,
  85.8,
  80.3,
  80.9,
  79.9],
 'Class Accuracy Non-Linear ConvBlock 5': [45.1,
  42.4,
  29.9,
  27.4,
  30.9,
  33.6,
  48.6,
  40.7,
  48.6,
  44.0],
 'Class Accuracy Rotation Task': [92.46, 92.53, 92.18, 91.73]}
In [6]:
# Supervised NIN
ev.evaluate_all(0, testloader, classes)
Evaluating Supervised NIN Classification Task:
Test Accuracy:  91.390 %
Test Accuracy of plane :  91.600 %
Test Accuracy of car :  95.900 %
Test Accuracy of bird :  87.000 %
Test Accuracy of cat :  83.200 %
Test Accuracy of deer :  92.100 %
Test Accuracy of dog :  85.500 %
Test Accuracy of frog :  94.800 %
Test Accuracy of horse :  93.900 %
Test Accuracy of ship :  94.700 %
Test Accuracy of truck :  95.200 %
Out[6]:
{'Accuracy Supervised NIN': 91.39,
 'Class Accuracy Supervised NIN': [91.6,
  95.9,
  87.0,
  83.2,
  92.1,
  85.5,
  94.8,
  93.9,
  94.7,
  95.2]}
In [6]:
# semi-supervised
ev.evaluate_all(-1, testloader, classes, semi=[20, 100, 400, 1000, 5000])
Evaluating Semi-supervised Experiment with 20 images per class:
Test Accuracy:  62.760 %
Test Accuracy of plane :  63.600 %
Test Accuracy of car :  74.300 %
Test Accuracy of bird :  46.500 %
Test Accuracy of cat :  37.200 %
Test Accuracy of deer :  57.000 %
Test Accuracy of dog :  59.400 %
Test Accuracy of frog :  76.900 %
Test Accuracy of horse :  66.000 %
Test Accuracy of ship :  71.700 %
Test Accuracy of truck :  75.000 %


Evaluating supervised NIN Experiment with 20 images per class:
Test Accuracy:  31.570 %
Test Accuracy of plane :  38.400 %
Test Accuracy of car :  42.600 %
Test Accuracy of bird :  17.300 %
Test Accuracy of cat :  24.300 %
Test Accuracy of deer :  23.900 %
Test Accuracy of dog :  28.200 %
Test Accuracy of frog :  35.300 %
Test Accuracy of horse :  36.000 %
Test Accuracy of ship :  37.400 %
Test Accuracy of truck :  32.300 %


--------------------------------------------------------------------------------


Evaluating Semi-supervised Experiment with 100 images per class:
Test Accuracy:  71.780 %
Test Accuracy of plane :  70.200 %
Test Accuracy of car :  82.800 %
Test Accuracy of bird :  58.300 %
Test Accuracy of cat :  52.200 %
Test Accuracy of deer :  67.100 %
Test Accuracy of dog :  67.000 %
Test Accuracy of frog :  81.000 %
Test Accuracy of horse :  75.900 %
Test Accuracy of ship :  80.300 %
Test Accuracy of truck :  83.000 %


Evaluating supervised NIN Experiment with 100 images per class:
Test Accuracy:  47.100 %
Test Accuracy of plane :  49.400 %
Test Accuracy of car :  61.000 %
Test Accuracy of bird :  29.700 %
Test Accuracy of cat :  30.000 %
Test Accuracy of deer :  37.200 %
Test Accuracy of dog :  39.800 %
Test Accuracy of frog :  50.000 %
Test Accuracy of horse :  54.100 %
Test Accuracy of ship :  64.900 %
Test Accuracy of truck :  54.900 %


--------------------------------------------------------------------------------


Evaluating Semi-supervised Experiment with 400 images per class:
Test Accuracy:  80.190 %
Test Accuracy of plane :  81.800 %
Test Accuracy of car :  89.500 %
Test Accuracy of bird :  72.900 %
Test Accuracy of cat :  61.600 %
Test Accuracy of deer :  79.700 %
Test Accuracy of dog :  72.500 %
Test Accuracy of frog :  86.800 %
Test Accuracy of horse :  83.800 %
Test Accuracy of ship :  85.700 %
Test Accuracy of truck :  87.600 %


Evaluating supervised NIN Experiment with 400 images per class:
Test Accuracy:  71.120 %
Test Accuracy of plane :  71.500 %
Test Accuracy of car :  81.700 %
Test Accuracy of bird :  56.800 %
Test Accuracy of cat :  53.200 %
Test Accuracy of deer :  67.200 %
Test Accuracy of dog :  62.000 %
Test Accuracy of frog :  77.800 %
Test Accuracy of horse :  76.700 %
Test Accuracy of ship :  82.900 %
Test Accuracy of truck :  81.400 %


--------------------------------------------------------------------------------


Evaluating Semi-supervised Experiment with 1000 images per class:
Test Accuracy:  83.670 %
Test Accuracy of plane :  83.900 %
Test Accuracy of car :  91.600 %
Test Accuracy of bird :  77.900 %
Test Accuracy of cat :  69.300 %
Test Accuracy of deer :  83.200 %
Test Accuracy of dog :  78.600 %
Test Accuracy of frog :  88.100 %
Test Accuracy of horse :  87.700 %
Test Accuracy of ship :  88.500 %
Test Accuracy of truck :  87.900 %


Evaluating supervised NIN Experiment with 1000 images per class:
Test Accuracy:  81.370 %
Test Accuracy of plane :  82.500 %
Test Accuracy of car :  90.200 %
Test Accuracy of bird :  73.900 %
Test Accuracy of cat :  64.200 %
Test Accuracy of deer :  78.000 %
Test Accuracy of dog :  75.000 %
Test Accuracy of frog :  86.600 %
Test Accuracy of horse :  85.100 %
Test Accuracy of ship :  90.600 %
Test Accuracy of truck :  87.600 %


--------------------------------------------------------------------------------


Evaluating Semi-supervised Experiment with 5000 images per class:
Test Accuracy:  88.940 %
Test Accuracy of plane :  89.000 %
Test Accuracy of car :  92.200 %
Test Accuracy of bird :  86.100 %
Test Accuracy of cat :  79.200 %
Test Accuracy of deer :  88.700 %
Test Accuracy of dog :  83.300 %
Test Accuracy of frog :  92.500 %
Test Accuracy of horse :  91.200 %
Test Accuracy of ship :  93.700 %
Test Accuracy of truck :  93.500 %


Evaluating supervised NIN Experiment with 5000 images per class:
Test Accuracy:  91.370 %
Test Accuracy of plane :  92.100 %
Test Accuracy of car :  95.900 %
Test Accuracy of bird :  87.800 %
Test Accuracy of cat :  81.200 %
Test Accuracy of deer :  92.100 %
Test Accuracy of dog :  86.900 %
Test Accuracy of frog :  94.100 %
Test Accuracy of horse :  93.300 %
Test Accuracy of ship :  95.400 %
Test Accuracy of truck :  94.900 %


--------------------------------------------------------------------------------


Out[6]:
{'Accuracy Semi-supervised 100': 71.78,
 'Accuracy Semi-supervised 1000': 83.67,
 'Accuracy Semi-supervised 20': 62.76,
 'Accuracy Semi-supervised 400': 80.19,
 'Accuracy Semi-supervised 5000': 88.94,
 'Accuracy Supervised NIN 100': 47.1,
 'Accuracy Supervised NIN 1000': 81.37,
 'Accuracy Supervised NIN 20': 31.57,
 'Accuracy Supervised NIN 400': 71.12,
 'Accuracy Supervised NIN 5000': 91.37,
 'Class Accuracy Semi-supervised 100': [70.2,
  82.8,
  58.3,
  52.2,
  67.1,
  67.0,
  81.0,
  75.9,
  80.3,
  83.0],
 'Class Accuracy Semi-supervised 1000': [83.9,
  91.6,
  77.9,
  69.3,
  83.2,
  78.6,
  88.1,
  87.7,
  88.5,
  87.9],
 'Class Accuracy Semi-supervised 20': [63.6,
  74.3,
  46.5,
  37.2,
  57.0,
  59.4,
  76.9,
  66.0,
  71.7,
  75.0],
 'Class Accuracy Semi-supervised 400': [81.8,
  89.5,
  72.9,
  61.6,
  79.7,
  72.5,
  86.8,
  83.8,
  85.7,
  87.6],
 'Class Accuracy Semi-supervised 5000': [89.0,
  92.2,
  86.1,
  79.2,
  88.7,
  83.3,
  92.5,
  91.2,
  93.7,
  93.5],
 'Class Accuracy Supervised NIN 100': [49.4,
  61.0,
  29.7,
  30.0,
  37.2,
  39.8,
  50.0,
  54.1,
  64.9,
  54.9],
 'Class Accuracy Supervised NIN 1000': [82.5,
  90.2,
  73.9,
  64.2,
  78.0,
  75.0,
  86.6,
  85.1,
  90.6,
  87.6],
 'Class Accuracy Supervised NIN 20': [38.4,
  42.6,
  17.3,
  24.3,
  23.9,
  28.2,
  35.3,
  36.0,
  37.4,
  32.3],
 'Class Accuracy Supervised NIN 400': [71.5,
  81.7,
  56.8,
  53.2,
  67.2,
  62.0,
  77.8,
  76.7,
  82.9,
  81.4],
 'Class Accuracy Supervised NIN 5000': [92.1,
  95.9,
  87.8,
  81.2,
  92.1,
  86.9,
  94.1,
  93.3,
  95.4,
  94.9]}

Plots

In [7]:
p.plot_all([20, 100, 400, 1000, 5000])